image analysis in biotechnological processes: applications to

ANTÓNIO LUÍS PEREIRA DO AMARAL

IMAGE ANALYSIS IN BIOTECHNOLOGICAL PROCESSES:

APPLICATIONS TO WASTEWATER TREATMENT

DISSERTATION FOR PHD DEGREE IN CHEMICAL AND BIOLOGICAL ENGINEERING AT THE UNIVERSITY OF MINHO

UNIVERSIDADE DO MINHO ESCOLA DE ENGENHARIA

DEPARTAMENTO DE ENGENHARIA BIOLÓGICA 2003

THESIS PRESENTED AT THE UNIVERSITY OF MINHO IN 29TH OF SEPTEMBER OF 2003

MEMBERS OF THE JURY:

MANUEL JOSÉ MAGALHÃES GOMES MOTA (PRESIDENT OF THE JURY)

FULL PROFESSOR AT UNIVERSIDADE DO MINHO

MARIE-NÖELLE PONS RESEARCH DIRECTOR AT CENTRE NATIONALE DE LA RECHERCHE

SCIENTIFIQUE (NANCY, FRANCE)

MARIA DE ASCENSÃO CARVALHO MIRANDA REIS ASSISTANT PROFESSOR AT UNIVERSIDADE NOVA DE LISBOA

MARIA MADALENA DOS SANTOS ALVES ASSISTANT PROFESSOR AT UNIVERSIDADE DO MINHO

JOSÉ ANTÓNIO COUTO TEIXEIRA FULL PROFESSOR AT UNIVERSIDADE DO MINHO

EUGÉNIO MANUEL DE FARIA CAMPOS FERREIRA ASSOCIATE PROFESSOR AT UNIVERSIDADE DO MINHO

THESIS UNDER THE SUPERVISION OF

EUGÉNIO MANUEL DE FARIA CAMPOS FERREIRA ASSOCIATE PROFESSOR

MANUEL JOSÉ MAGALHÃES GOMES MOTA

FULL PROFESSOR

UNIVERSITY OF MINHO DEPARTAMENTO DE ENGENHARIA BIOLÓGICA

2003

This thesis was supported by the III Quadro Comunitário de Apoio of the Fundo Social Europeu by means of a Fundação para a Ciência e Tecnologia PRAXIS XXI/BD/20325/99 PhD grant. The several stays in Nancy (France) were supported by the 203/B4 grant of the convene between Instituto para a Cooperação Científica e Tecnológica Internacional and Ambassade de France au Portugal.

Esta tese é dedicada muito em especial à minha família por todo o apoio, carinho e encorajamento demonstrado ao longo destes anos.

(This thesis is dedicated to my family for all their support, affection and encouragement throughout all these years)

Acknowledgements

António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment 1

ACKNOWLEDGEMENTS

O autor gostaria de expressar a sua gratidão a todos os que o acompanharam no decurso deste trabalho, com especial relevo aos seguintes.

Ao orientador, Doutor Eugénio Campos Ferreira, e co-orientador Professor Manuel Mota por todo o apoio prestado e disponibilidade demonstrada, bem como todo o encorajamento e conselhos outorgados durante o decorrer deste trabalho especialmente ao nível das técnicas de análise de imagem e de estatística multivariável.

À Doutora Madalena Alves, por todo o apoio prestado na compreensão dos fenómenos envolvidos na digestão anaeróbia, bem como pela disponibilidade demonstrada, encorajamento e conselhos prestados no decorrer deste trabalho.

Je voudrais remmercier au Professeurs Marie-Nöelle Pons et Hervé Vivier qui m’on permis d’effectuer les séjours au Laboratoire des Sciences du Génie Chimique (LSGC) à Nancy et pour leurs nombreuses conseils sur les techniques d’analyse d’images et de statistique multivariable.

Aos Doutores Maurício da Motta, Alcina Pereira, Nicole Dias e Maria Alice Coelho e aos Mestres Lúcia Neves e Pablo Araya-Kroff pelo intercâmbio de experiências e trabalhos efectuados, bem como pela amizade demonstrada e encorajamento recebido no decorrer deste trabalho.

Aos Doutores Nelson Lima, Ana Nicolau e José Maria Oliveira pela disponibilidade demonstrada, encorajamento recebido e conselhos outorgados no decorrer deste trabalho.

À AGERE E.M. (Braga, Portugal), e às Engenheiras Sofia Rodrigues e Raquel Pereira, pelo intercâmbio de experiências e trabalhos efectuados especialmente ao nível do trabalho referente à monitorização de lamas activadas, bem como pela disponibilidade demonstrada durante o decorrer deste trabalho.

Às instituições que albergaram e permitiram a realização deste trabalho, Departamento de Engenharia Biológica, Universidade do Minho e Laboratoire des Sciences du Génie Chimique.

Às instituições e organismos que contribuíram financeiramente para a consecução deste trabalho, Fundação para a Ciência e Tecnologia, Instituto para a Cooperação Científica e Tecnológica Internacional e Ambassade de France au Portugal.

À Escola Superior de Tecnologia e de Gestão e ao Instituto Politécnico de Bragança pela compreensão demonstrada e tempo libertado para a escrita desta tese.

A todos os colegas de trabalho por todo o apoio prestado, disponibilidade demonstrada, encorajamento e conselhos outorgados.

A todos os amigos pela amizade demonstrada e encorajamento recebido ao longo destes anos.

Acknowledgements

António Luís Pereira do Amaral 2 Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Thesis Organization


THESIS ORGANIZATION In the organization of this thesis it was seek to integrate the different subjects

within its field in a homogeneous way. The initial sections of the thesis cover aspects such as the acknowledgements, thesis organization, abstract, table of contents and list of figures, tables, symbols and abbreviations.

The ‘General Introduction’ section aims at introducing the background theoretical fundaments and objectives of this work and is divided into four parts: image processing and analysis in biotechnology, brief introduction to wastewater treatments, aerobic wastewater treatment processes and anaerobic wastewater treatment processes. In the ‘Image Processing and Analysis in Biotechnology’ section a short bibliographic review on the main application fields of image analysis and specifically in biotechnology is reported. Moreover, a brief introduction to image acquisition, processing and analysis techniques is further presented. In the ‘Brief Introduction to Wastewater Treatments’ section a short introduction to these treatments importance and worldwide applications is provided. Furthermore, the wastewater operating parameters are discussed. In the ‘Aerobic Wastewater Treatment’ section this process is discussed both from the activated sludge viewpoint as from the protozoa and metazoa viewpoint. The ‘Anaerobic Wastewater Treatment’ section is discussed taking into consideration its basis and technology with a special focus on the granulation process and the granule deterioration problematic.

The ‘Materials and Methods’ section is intended to elucidate the framework of this study, and is divided into five parts: experimental surveys, operating parameters, image processing, morphological parameters and multivariable statistical techniques. In the ‘Experimental Surveys’ section the experimental set-up, operating parameters and image acquisition methodologies for the activated sludge monitoring, protozoa and metazoa identification, granulation process and granule deterioration are explained. In the ‘Operating Parameters’ section the measurements made for both the activated and the anaerobic sludge characterization works are described. In the ‘Image Processing’ section a full description of the programmes is provided emphasizing the different stages within each one. A schematic representation and resulting images from the main steps of the programmes are also provided at the end of each programme description. In the ‘Morphological Parameters’ section the determined parameters for the activated sludge and the anaerobic sludge characterization are described. In the ‘Multivariable Statistical Techniques’ section a brief description is provided on the Discriminant Analysis, Neural Networks and Partial Least Squares techniques. However, it is not the intention of this section to thoroughly describe these techniques and thus only a brief report is presented.

In the ‘Results and Discussion’ section are presented the main results obtained for both the aerobic and anaerobic wastewater treatment processes, namely the protozoa and metazoa identification, activated sludge monitoring, anaerobic granulation process monitoring and granule deterioration triggered by oleic acid. Furthermore, it was intended to provide for each result a brief discussion on the key conclusions.

In the ‘Conclusions and Recommendations’ section are presented the main conclusions obtained for both the aerobic and anaerobic wastewater treatment processes, namely the protozoa and metazoa identification, activated sludge monitoring, anaerobic granulation process monitoring and granule deterioration triggered by oleic acid. Moreover, a brief discussion on the key aspects of the results alongside the main overall

Thesis Organization


conclusions is presented as well as some recommendations that should be taken into account with respect to future studies on these subjects.

Enclosing the body of this thesis the bibliographic references are provided whereas an author index clarifies the text location of the referred authors. Finally, in appendix is made available a short protozoa and metazoa guide, the calibration of the morphological parameters, the enclosed CD contents and the author’s publication list.

In terms of the notation used in this thesis, the image analysis and operating parameters are referred in capital letters and italic, as well as the multivariable statistical techniques and the programmed software, commercial software and hardware, whereas the symbols are presented in italic.

Abstract


ABSTRACT In this work four different studies were proposed with a common interest in image

analysis methodologies and multivariable statistical techniques yet covering distinct objectives.

In an activated sludge study, the aggregates and filamentous bacteria contents and morphology were surveyed by image analysis methodologies. The high Sludge Volume Index values denoted the existence of a severe bulking problem of non-zoogleal nature as pointed out by the predominance of normal flocs. Moreover, the high filamentous bacteria per suspended solids ratio values clearly indicated the existence of a filamentous bulking problem, and were able to follow, at some extent, the Sludge Volume Index behaviour. Furthermore, the Partial Least Squares analysis revealed a strong relationship between the Total Suspended Solids and the Total Aggregates Area, although it must be emphasized though that for a wastewater treatment plant working in good operating conditions this relationship may not stand true.

In a protozoa and metazoa identification work the main objective resided on the development of an image analysis programme to morphologically characterize the protozoa and metazoa and treat the collected data by multivariable statistical techniques. The studied species attained a satisfactory overall recognition level in terms of global recognition and misclassification performances, whereas for the main protozoa and metazoa groups as well as for the ciliated protozoa groups, the results were quite good. Such was also the case for the plant conditions assessment as effluent quality, aeration, sludge age, and nitrification presence. However, the assessment of critical conditions such as low effluent quality, low aeration and fresh sludge, proved to be poorer. Comparing the two multivariable statistical techniques, the overall results were lower for the Neural Networks than for the Discriminant Analysis with the exception of the critical conditions assessment.

Regarding the anaerobic granulation process, image analysis methodologies were used to follow morphological changes in the granulation process. This survey allowed for the determination of an overall aggregates size and contents increase throughout the experiment as well as the establishment of the granulation time with the formation of granular structures. It was also possible to identify an initial stage involving the predominant growth of the filamentous bacteria followed by a second stage of aggregates growth using the filamentous bacteria as a backbone and a final stage of balanced filamentous bacteria and aggregates contents growth. Moreover, the strong Up-Flow Velocity and Organic Loading Rate increases led to disturbances within the reactor such as the liberation of filamentous bacteria and aggregates size changes.

Concerning the granule deterioration triggered by oleic acid study, the results obtained for the outgoing effluent Volatile Suspended Solids reflected a biomass wash-out phenomenon throughout the experiment. Furthermore, the aggregates morphological survey allowed determining a decreasing trend in the aggregates size, as well as an aggregate stratification with the larger aggregates in the top section of the reactor. It could also be established that the granule deterioration process triggered by oleic acid led to more freely dispersed structures in terms of filamentous bacteria and lighter aggregates which ultimately rose to the top of the reactor, where the lighter ones suffered a wash-out phenomenon.

Abstract


Resumo


RESUMO Neste trabalho, foram propostos quatro estudos diferentes utilizando técnicas de

análise de imagem e de estatística multivariável com objectivos distintos.

Num estudo das lamas activadas foram examinados o conteúdo e morfologia de agregados e bactérias filamentosas por metodologias da análise de imagem. Os valores elevados do índice volumétrico de lamas denotaram a existência de um problema de “bulking” severo de natureza não-zoogleal como indicado pelo predomínio de flocos normais. Adicionalmente, os valores elevados da razão entre as bactérias filamentosas e os sólidos suspensos apontam claramente para a existência de um problema de “bulking” filamentoso, e mimetizaram, em certa medida, o comportamento do índice volumétrico de lamas. De referir ainda que uma análise dos mínimos dos quadrados parciais revelou uma correlação entre os sólidos suspensos totais e a área total dos agregados, devendo contudo ser referido que para estações de tratamento de águas residuais funcionando em boas condições a correlação obtida poderá não se manter válida.

Num estudo de identificação dos protozoários e metazoários os objectivos do trabalho consistiram no desenvolvimento de um programa da análise de imagem para a sua caracterização morfológica e tratamento dos dados por técnicas de estatística multivariável. A identificação dos protozoários e metazoários revelou-se satisfatória em termos de reconhecimento global e da classificação errónea, enquanto que para os principais grupos de protozoários e metazoários bem como de ciliados foi bastante elevada. O mesmo se passou na aferição das condições de operação tais como a qualidade do efluente, arejamento, idade das lamas e nitrificação. Contudo, na determinação das condições críticas de funcionamento como baixa qualidade do efluente, arejamento insuficiente e lamas jovens os resultados foram apenas razoáveis. Comparando as duas técnicas de estatística multivariável utilizadas, os resultados globais foram inferiores para as redes neuronais do que para a análise discriminante com a excepção da determinação de condições críticas de funcionamento.

Na monitorização do processo anaeróbio de granulação, foram usadas metodologias de análise de imagem para seguir as mudanças morfológicas. A aferição da morfologia dos agregados permitiu a determinação do aumento em tamanho e conteúdo dos agregados ao longo da experiência, bem como na determinação do tempo de granulação. Foi também possível identificar uma fase inicial de crescimento preferencial das bactérias filamentosas, seguida por uma segunda fase de crescimento dos agregados utilizando as bactérias filamentosas como espinha dorsal e uma fase final de crescimento balanceado entre as bactérias filamentosas e os agregados. De referir ainda que os fortes aumentos da velocidade ascencional e carga orgânica provocaram alterações no reactor como a libertação de bactérias filamentosas e modificação do tamanho dos agregados.

Em relação ao processo de desgranulação devido a alimentação com oleato, os resultados obtidos para os sólidos suspensos voláteis reflectiram a lavagem da biomassa ao longo da experiência. Adicionalmente, a aferição da morfologia dos agregados permitiu distinguir uma tendência decrescente do tamanho dos mesmos ao longo da experiência bem como a sua estratificação com os maiores a situarem-se no topo do reactor. Foi ainda possível estabelecer que o processo de desgranulação resultou numa estrutura mais dispersa em termos das bactérias filamentosas e agregados mais leves que migraram para o topo do reactor onde os mais leves sofreram um fenómeno de lavagem.

Resumo


Résumé


RÉSUMÉ Dans ce travail, quatre études différentes ont été proposés basées sur l’utilisation

des techniques d´analyse d’image et statistique multivariable avec des objectifs distincts.

Concernant l’étude de la surveillance des boues activées, le contenu et la morphologie des agrégats et des bactéries filamenteuses ont été examinés par des méthodologies d'analyse d'image. Les valeurs très élevées de l'index volumétrique des boues et la prédominance des flocs normaux dénotent l'existence d'un problème de bulking sérieux de nature non-zoogléale. D'ailleurs, les hautes valeurs du rapport des bactéries filamenteuses aux matières en suspension ont clairement indiqué l'existence d'un problème de bulking filamenteux. En fait, ce paramètre s'est avéré satisfaisant pour suivre le comportement de l'index volumétrique des boues. En outre, l'analyse des moindres carrés partielle a indiqué un rapport entre les matières en suspension et la surface totale d'agrégats. On doit souligner cependant que pour des stations fonctionnant dans de bonnes conditions les corrélations obtenues peuvent ne pas être valables.

L'objectif principal du travail d'identification des protozoaires et métazoaires a résidé dans le le développement d'un programme d'analyse d'image pour les caractériser morphologiquement et traiter les données rassemblées par des techniques statistiques multivariables. Pour les espèces étudiées on a atteint des niveaux globaux d'identification et de mauvaise classification satisfaisants, tandis que pour les groupes principaux de protozoaires et métazoaires ainsi que pour les ciliés, les résultats ont pu être considérés tout à fait bons. C’est également le cas pour l'évaluation des conditions de fonctionnement comme l’âge des boues, aération, nitrification et qualité de l’effluent final. Cependant, l'évaluation des conditions critiques telles que la mauvaise qualité de l’effluent, la mauvaise aération et les boues fraiches était moins bonne. Comparant les techniques statistiques, les résultats globaux étaient inférieurs pour les réseaux neuronaux que pour l'analyse discriminante sauf pour l'évaluation des conditions critiques.

Concernant l'expérience anaérobie de granulation, des méthodologies d'analyse d'image ont été employées pour suivre les changements morphologiques du processus de granulation. Le suivi de la morphologie d’agrégats a tenu compte de l'augmentation du contenu et de la taille des agrégats aussi bien que la détermination du temps de granulation. Il a été également possible d'identifier une première étape comportant la croissance prédominante des bactéries filamenteuses suivie d'une deuxième étape de croissance d'agrégats en utilisant les bactéries filamenteuses comme squelette et d'une étape finale de croissance équilibrée entre les bactéries filamenteuses et les agrégats. D'ailleurs, les fortes augmentations de la vitesse de flux ascendant et de la charge organique ont amené des perturbations dans le réacteur telles que la libération des bactéries filamenteuses et des changements de la taille d'agrégats.

En ce qui concerne le processus de dégranulation dû à l’oléate, les résultats obtenus pour les matières volatiles en suspension ont reflété un phénomène de lessivage de la biomasse. En outre, l’analyse morphologique des agrégats a permis de déterminer une tendance à la décroissance dans la taille d'agrégats aussi bien qu’une stratification avec les plus gros agrégats dans la section supérieure du réacteur. On pourrait aussi établir que le processus de dégranulation par l’oléate a amené à une structure plus librement dispersée en termes de bactéries filamenteuses et des agrégats plus légers qui ont été entrainés jusque dans le haut du réacteur, où certains d’entre eux ont été lessivés.

Résumé


Table of Contents


TABLE OF CONTENTS

ACKNOWLEDGEMENTS ....................................................................................................... 1 THESIS ORGANIZATION ..................................................................................................... 3 ABSTRACT ............................................................................................................................ 5 RESUMO ................................................................................................................................ 7 RÉSUMÉ................................................................................................................................. 9 TABLE OF CONTENTS ........................................................................................................ 11 LIST OF FIGURES ................................................................................................................ 15 LIST OF TABLES .................................................................................................................. 21 LIST OF SYMBOLS............................................................................................................... 23 LIST OF ABBREVIATIONS .................................................................................................. 29

1 GENERAL INTRODUCTION.................................................................................... 31

1.1 IMAGE PROCESSING AND ANALYSIS IN BIOTECHNOLOGY .................................... 33 1.1.1 SHORT REVIEW ON IMAGE ANALYSIS APPLICATIONS............................................... 34 1.1.2 FUNDAMENTS OF IMAGE CAPTURE, PROCESSING AND ANALYSIS .......................... 37 1.2 BRIEF INTRODUCTION TO WASTEWATER TREATMENT .......................................... 41 1.3 AEROBIC WASTEWATER TREATMENT PROCESSES .................................................. 45 1.3.1 THE ACTIVATED SLUDGE WASTEWATER TREATMENT PROCESS .............................. 46 1.3.1.1 The Activated Sludge Process Basis ................................................................... 46 1.3.1.2 The Activated Sludge Floc Structure.................................................................. 49 1.3.1.3 Perspective and Aim of Work ............................................................................. 52 1.3.2 PROTOZOA AND METAZOA IN ACTIVATED SLUDGE ................................................ 53 1.3.2.1 Protozoa and Metazoa Population Dynamics................................................... 53 1.3.2.2 Protozoa and Metazoa Systematic...................................................................... 55 1.3.2.3 Perspective and Aim of Work ............................................................................. 57 1.4 ANAEROBIC WASTEWATER TREATMENT PROCESSES ............................................. 61 1.4.1.1 Anaerobic Treatment Basis and Technology..................................................... 62 1.4.1.2 The Granulation Process ...................................................................................... 65 1.4.1.3 Granule Deterioration .......................................................................................... 67 1.4.1.4 Perspective and Aim of Work ............................................................................. 67

Table of Contents


2 MATERIALS AND METHODS................................................................................. 69

2.1 EXPERIMENTAL SURVEYS............................................................................................ 71 2.1.1 ACTIVATED SLUDGE MONITORING ........................................................................... 72 2.1.1.1 Activated Sludge Experimental Survey............................................................. 72 2.1.1.2 Activated Sludge Image Acquisition and Processing ...................................... 73 2.1.2 PROTOZOA AND METAZOA IDENTIFICATION ........................................................... 74 2.1.2.1 Protozoa and Metazoa Experimental Survey.................................................... 74 2.1.2.2 Protozoa and Metazoa Image Acquisition and Processing............................. 75 2.1.3 ANAEROBIC GRANULATION PROCESS MONITORING ............................................... 76 2.1.3.1 Anaerobic Granulation Experimental Survey................................................... 76 2.1.3.2 Anaerobic Granulation Image Acquisition and Processing............................ 77 2.1.4 GRANULE DETERIORATION TRIGGERED BY OLEIC ACID........................................... 79 2.1.4.1 Granule Deterioration Experimental Survey .................................................... 79 2.1.4.2 Granule Deterioration Image Acquisition and Processing ............................. 80 2.2 OPERATING PARAMETERS........................................................................................... 81 2.2.1 ACTIVATED SLUDGE OPERATING PARAMETERS ....................................................... 82 2.2.2 ANAEROBIC DIGESTION OPERATING PARAMETERS.................................................. 83 2.3 IMAGE PROCESSING .................................................................................................... 85 2.3.1 PROTOZOA AND METAZOA IMAGE PROCESSING...................................................... 86 2.3.2 FLOCS IMAGE PROCESSING ........................................................................................ 96 2.3.3 GRANULES IMAGE PROCESSING .............................................................................. 104 2.3.4 FILAMENTS IMAGE PROCESSING.............................................................................. 112 2.4 MORPHOLOGICAL PARAMETERS ............................................................................. 123 2.4.1 EUCLIDEAN MORPHOLOGICAL PARAMETERS......................................................... 124 2.4.2 FRACTAL DIMENSIONS............................................................................................. 132 2.5 MULTIVARIABLE STATISTICAL TECHNIQUES......................................................... 135 2.5.1 PARTIAL LEAST SQUARES......................................................................................... 136 2.5.2 DISCRIMINANT ANALYSIS........................................................................................ 138 2.5.3 NEURAL NETWORKS................................................................................................. 140

3 RESULTS AND DISCUSSION ................................................................................ 145

3.1 AEROBIC WASTEWATER TREATMENT PROCESSES................................................. 147 3.1.1 PROTOZOA AND METAZOA IDENTIFICATION ......................................................... 148 3.1.1.1 Discriminant Analysis ........................................................................................ 148

Table of Contents


3.1.1.2 Neural Networks................................................................................................. 153 3.1.2 ACTIVATED SLUDGE MONITORING ......................................................................... 158 3.1.2.1 Operating Parameters Monitoring ................................................................... 158 3.1.2.2 Morphological Parameters Monitoring ........................................................... 159 3.1.2.3 PLS Analysis ........................................................................................................ 168 3.2 ANAEROBIC WASTEWATER TREATMENT PROCESSES ........................................... 175 3.2.1 ANAEROBIC GRANULATION PROCESS MONITORING ............................................. 176 3.2.1.1 Operating Parameters Monitoring ................................................................... 176 3.2.1.2 Dilution Study ..................................................................................................... 178 3.2.1.3 Morphological Parameters Monitoring ........................................................... 180 3.2.2 GRANULE DETERIORATION TRIGGERED BY OLEIC ACID......................................... 193 3.2.2.1 Operating Parameters Monitoring ................................................................... 193 3.2.2.2 Morphological Parameters Monitoring ........................................................... 195 3.2.2.3 Fines Fraction Monitoring ................................................................................. 206

4 CONCLUSIONS AND RECOMMENDATIONS................................................. 209

4.1 AEROBIC WASTEWATER TREATMENT PROCESSES ................................................ 211 4.1.1 PROTOZOA AND METAZOA IDENTIFICATION......................................................... 212 4.1.2 ACTIVATED SLUDGE MONITORING ......................................................................... 214 4.1.3 RECOMMENDATIONS ............................................................................................... 216 4.2 ANAEROBIC WASTEWATER TREATMENT PROCESSES ........................................... 219 4.2.1 ANAEROBIC GRANULATION PROCESS MONITORING ............................................. 220 4.2.2 GRANULE DETERIORATION TRIGGERED BY OLEIC ACID......................................... 222 4.2.3 RECOMMENDATIONS ............................................................................................... 224 BIBLIOGRAPHY................................................................................................................. 225 AUTHOR INDEX................................................................................................................ 235 APPENDIX A: SHORT PROTOZOA AND METAZOA GUIDE ................................................. I APPENDIX B: CALIBRATION OF THE MORPHOLOGICAL PARAMETERS ......................... IX APPENDIX C: CD CONTENTS ...........................................................................................XXI APPENDIX D: PUBLICATION LIST................................................................................. XXIII

Table of Contents


List of Figures


LIST OF FIGURES

Figure 1.1 – Activated sludge wastewater treatment process (adapted from Canler et al., 1999). ....................................................................................................................................... 46

Figure 1.2 – Main trophic relationships in an aeration tank (adapted from Canler et al., 1999). ....................................................................................................................................... 48

Figure 1.3 – Floc morphology of the most common malfunctions found in activated sludge: a) normal flocs. b) pin-point flocs. c) filamentous bulking. d) zoogleal bulking................................................................................................................................................... 51

Figure 1.4 – Microbial population as a function of sludge age (adapted from Canler et al., 1999). (1 - Bacteria; 2 - Zooflagellates; 3 - Free-swimming and crawling ciliates; 4 - Sessile ciliates; 5 - Rotifers)................................................................................................... 54

Figure 1.5 – Bacterial groups involved in anaerobic digestion (adapted from Gujer and Zehnder, 1983). ...................................................................................................................... 63

Figure 1.6 – Up-flow anaerobic sludge blanket (UASB) reactor (adapted from Lettinga et al., 1980)................................................................................................................................... 65

Figure 1.7 – Anaerobic biomass agglomerates: a) flocs. b) pellets. c) granules. ................... 66 Figure 2.1 – Braga wastewater treatment plant. ....................................................................... 72 Figure 2.2 – Image acquisition methodology within each slide. ............................................ 73 Figure 2.3 – Nancy wastewater treatment plant. ...................................................................... 74 Figure 2.4 – Experimental Set-Up: a) EGSB. b) Internal settler. c) External settler. d) Biogas

flow-meter. e) Recycle f) Feed containers (adapted from Araya-Kroff et al., 2002). .... 76 Figure 2.5 – Image acquisition methodology within each Petri dish..................................... 78 Figure 2.6 – Experimental Set-Up. a) EGSB b) Internal settler c) Feeding d) Biogas flow-

meter d) External settler e) Recycle f) Treated effluent.................................................... 79 Figure 2.7 – User parameters dialog box.................................................................................... 87 Figure 2.8 – Region of Interest (ROI) dialog box....................................................................... 88 Figure 2.9 – Segmentation dialog box. ....................................................................................... 90 Figure 2.10 – Euclidean distance map of an object. .................................................................. 92 Figure 2.11 – Schematic representation of ProtoRec programme............................................ 94 Figure 2.12 – Resulting images from the main steps of the ProtoRec programme (the

numbers in brackets refer to the step number). ................................................................ 95 Figure 2.13 – User parameters dialog box.................................................................................. 97 Figure 2.14 – Schematic representation of Flocs programme ................................................ 102 Figure 2.15 – Resulting images from the main steps of the Flocs programme (the numbers

in brackets refer to the step number)................................................................................ 103

List of Figures


Figure 2.16 – User parameters dialog box................................................................................ 105 Figure 2.17 – Histogram minima, ascent inflection point and descent inflection point.... 106 Figure 2.18 – Schematic representation of Granules programme.......................................... 110 Figure 2.19 – Resulting images from the main steps of the Granules programme (the

numbers in brackets refer to the step number). .............................................................. 111 Figure 2.20 – User parameters dialog box................................................................................ 113 Figure 2.21 – Schematic representation of Filaments programme......................................... 118 Figure 2.22 – Resulting images from the main steps of the Filaments programme (the

numbers in brackets refer to the step number). .............................................................. 119 Figure 2.23 – Representation of the projected image of an object and the morphological

parameters Deq, FMax and FMin. ........................................................................................... 125 Figure 2.24 – Representation of the free filaments.................................................................. 126 Figure 2.25 – Representation of the bounding box width and length.................................. 128 Figure 2.26 – Representation of the Convex Envelope. ............................................................. 129 Figure 2.27 – Representation of the complement of the object. ............................................ 129 Figure 2.28 – Border box, interior box and empty box (larger squares). ............................. 132 Figure 2.29 – Graphical representation of a fractal dimension. ............................................ 133 Figure 2.30 – A model of an artificial neuron. ......................................................................... 140 Figure 2.31 – Main activated functions: a) hard-limit threshold b) saturated linear

threshold (b1-positive range; b2- total range) c) sigmoid (c1- logistic; c2- bipolar logistic; c3- Gaussian).......................................................................................................... 141

Figure 2.32 – Simple neural network. ....................................................................................... 142 Figure 2.33 – A single two-input, one-output perceptron with a bias. ................................ 143 Figure 3.1 – Sludge Volume Index and Total Suspended Solids throughout the survey......... 158 Figure 3.2 – Total Aggregates Area throughout the survey. .................................................... 159 Figure 3.3 – Aggregates Equivalent Diameter throughout the survey..................................... 159 Figure 3.4 – Aggregates Equivalent Diameter throughout the survey..................................... 160 Figure 3.5 – Aggregates Number throughout the survey. ........................................................ 161 Figure 3.6 – Aggregates Number Percentage distribution throughout the survey................. 162 Figure 3.7 – Aggregates Area Percentage distribution throughout the survey. ..................... 163 Figure 3.8 – Evolution of the morphological parameters for each size class. ..................... 164 Figure 3.9 – Filament Length and Total Filaments Length throughout the survey................. 165 Figure 3.10 – Total Filaments Length per Total Aggregates Area Ratio throughout the survey.

................................................................................................................................................ 166 Figure 3.11 – Total Filaments Length per Total Suspended Solids Ratio throughout the survey.

................................................................................................................................................ 166

List of Figures


Figure 3.12 – Filamentous bacteria images of some key days in the activated sludge survey (the bar represents 100 µm)................................................................................................ 167

Figure 3.13 – Macroscopic aggregates images of some key days in the activated sludge survey (the bar represents 500 µm)................................................................................... 168

Figure 3.14 – PLS analysis for the SVI on the free filamentous bacteria contents, free filamentous bacteria characterization, aggregates contents and aggregates morphology.......................................................................................................................... 169

Figure 3.15 – Global PLS analysis for the SVI. ........................................................................ 170 Figure 3.16 – Sludge Volume Index vs. Total Filament Length per Total Aggregates Area Ratio.

................................................................................................................................................ 170 Figure 3.17 – Observed vs. predicted Sludge Volume Index. ................................................... 171 Figure 3.18 – Sludge Volume Index vs. Total Filament Length per Total Suspended Solids Ratio.

................................................................................................................................................ 171 Figure 3.19 - PLS analysis for the TSS on the free filamentous bacteria contents, aggregates

contents and aggregates morphology. ............................................................................. 172 Figure 3.20 – Global PLS analysis for the TSS. ........................................................................ 173 Figure 3.21 – Total Suspended Solids vs. Total Aggregates Area. ............................................... 174 Figure 3.22 – Observed vs. predicted Total Suspended Solids. ................................................ 174 Figure 3.23 – Hydraulic Retention Time and Up-Flow Velocity throughout the granulation

process................................................................................................................................... 177 Figure 3.24 – Organic Loading Rate and COD removal percentage throughout the

granulation process. ............................................................................................................ 177 Figure 3.25 – Outgoing effluent Volatile Suspended Solids throughout the granulation

process................................................................................................................................... 178 Figure 3.26 – Area Recognition Percentage throughout the granulation process. ................. 179 Figure 3.27 – Flocs and Filaments Number per image and Area Recognition Percentage. ...... 179 Figure 3.28 – Flocs, Filaments and Granules Number per image and Area Recognition

Percentage. ............................................................................................................................. 180 Figure 3.29 – Flocs, Filaments and Granules Number per image and Area Recognition

Percentage. ............................................................................................................................. 180 Figure 3.30 – Aggregates Equivalent Diameter throughout the granulation process. ........... 181 Figure 3.31 – Aggregates Equivalent Diameter throughout the granulation process. ........... 182 Figure 3.32 – Aggregates Number Percentage distribution throughout the granulation

process................................................................................................................................... 183 Figure 3.33 – Aggregates Area Percentage distribution throughout the granulation process.

................................................................................................................................................ 184 Figure 3.34 – Evolution of the morphological parameter for each size class...................... 186 Figure 3.35 – Filament Length throughout the granulation process. ..................................... 187

List of Figures


Figure 3.36 – Total Filaments Length per Total Aggregates Area Ratio throughout the granulation process. ............................................................................................................ 188

Figure 3.37 – Total Filaments Length per Volatile Suspended Solids Ratio throughout the granulation process. ............................................................................................................ 188

Figure 3.38 – Filamentous bacteria images of some key days in the granulation experiment (the bar represents 100 µm)................................................................................................ 190

Figure 3.39 – Microscopic aggregates images of some key days in the granulation experiment (the bar represents 100 µm)........................................................................... 191

Figure 3.40 – Macroscopic aggregates images of some key days in the granulation experiment (the bar represents 500 µm)........................................................................... 192

Figure 3.41 – Organic Loading Rate and COD Removal Percentage throughout the experiment time. ....................................................................................................................................... 194

Figure 3.42 – Outgoing effluent Volatile Suspended Solids throughout the experiment time................................................................................................................................................. 194

Figure 3.43 – Aggregates Equivalent Diameter throughout the experiment time. ................. 195 Figure 3.44 – Aggregates Equivalent Diameter throughout the experiment time. ................. 196 Figure 3.45 – Aggregates Number Percentage distribution throughout the experiment time.

................................................................................................................................................ 197 Figure 3.46 – Aggregates Area Percentage distribution throughout the experiment time. .. 197 Figure 3.47 – Evolution of the morphological parameters for each size class. ................... 199 Figure 3.48 – Filament Length throughout the experiment time. ........................................... 200 Figure 3.49 – Total Filaments Length per Total Aggregates Area Ratio throughout the

experiment time. .................................................................................................................. 201 Figure 3.50 – Bottom filamentous bacteria images of some key days in the granule

deterioration experiment (the bar represents 100 µm)................................................... 202 Figure 3.51 – Top filamentous bacteria images of some key days in the granule

deterioration experiment (the bar represents 100 µm)................................................... 203 Figure 3.52 – Bottom macroscopic aggregates images of some key days in the granule

deterioration experiment (the bar represents 1 mm)...................................................... 204 Figure 3.53 – Top macroscopic aggregates images of some key days in the granule

deterioration experiment (the bar represents 1 mm)...................................................... 205 Figure 3.54 – Fines Area and Weight Percentages distribution throughout the experiment

time. ....................................................................................................................................... 206 Figure 3.55 – Correlation between the Fines Area Percentage and the Fines Weight Percentage.

................................................................................................................................................ 207 Figure 3.56 – Correlations between the TL/TA and the Fines Area Percentage for the top and

bottom sections of the reactor............................................................................................ 207

Figure I – Objects used for the morphological parameters distinction................................... ix Figure II – Objects used in the morphological parameters calibration.................................... x

List of Figures


Figure III – Objects used in the fractal dimensions fitness. ....................................................... x Figure IV – Results obtained for the morphological parameters distinction......................... xi Figure V – Results obtained for the morphological parameters calibration. .......................xiv Figure VI – Results obtained for the fractal dimensions fitness. ..........................................xvii

List of Figures


List of Tables


LIST OF TABLES

Table 1.1 – Typical characteristics of domestic wastewater (adapted from Tchobanoglous and Burton, 1991). ................................................................................................................. 43

Table 1.2 – Comparison of the operating conditions between aerobic and anaerobic treatment processes (adapted from Brouzes, 1973). ......................................................... 44

Table 1.3 – Values of the activated sludge treatment operating conditions for domestic sewage (adapted from Forster, 1977).................................................................................. 47

Table 1.4 – Comparison of physiological characteristics of floc-forming and filamentous bacteria (adapted from Sykes, 1989). .................................................................................. 49

Table 1.5 – Relationship between sludge age and organic load (adapted from Canler et al., 1999). ....................................................................................................................................... 53

Table 1.6 – Relationships between dominant groups, efficiency and problem causes (adapted from Nicolau et al., 1997). .................................................................................... 55

Table 1.7 – Most common ciliate protozoa in wastewater treatment (adapted from Madoni, 1994a)....................................................................................................................... 57

Table 1.8 – Studied protozoa........................................................................................................ 58 Table 1.9 – Relationships between protozoa and metazoa and plant operation conditions

(NI represents no indication). .............................................................................................. 59 Table 3.1 – Micro-organisms recognition (number and percentage). .................................. 149 Table 3.2 – Recognition and misclassification levels. ............................................................. 150 Table 3.3 – Main protozoa and metazoa group’s recognition (number and percentage). 150 Table 3.4 – Main ciliates group’s recognition (number and percentage). ........................... 151 Table 3.5 – Effluent quality assessment (number and percentage). ..................................... 152 Table 3.6 – Aeration assessment (number and percentage). ................................................. 152 Table 3.7 – Sludge age assessment (number and percentage). ............................................. 152 Table 3.8 – Nitrification assessment (number and percentage). ........................................... 152 Table 3.9 – Micro-organisms recognition (number and percentage). .................................. 154 Table 3.10 – Recognition and misclassification levels. ........................................................... 155 Table 3.11 – Main protozoa and metazoa group’s recognition (number and percentage).

................................................................................................................................................ 155 Table 3.12 – Main ciliates group’s recognition (number and percentage). ......................... 156 Table 3.13 – Effluent quality assessment (number and percentage). ................................... 156 Table 3.14 – Aeration assessment (number and percentage). ............................................... 156 Table 3.15 – Sludge age assessment (number and percentage). ........................................... 157

List of Tables


Table 3.16 – Nitrification assessment (number and percentage). ......................................... 157

List of Symbols


LIST OF SYMBOLS

A → Area of an object

a → Standard coefficient

Aant → Previous Erosion Area

AC → Convex Envelope Area

Aer → Area of an object after er erosions

Ai → Area of each i aggregate

Aj → Area of each j Fine aggregate

Ar → Area of an object within an r sized object

Area % → Area Percentage

AStk → Stalk Area

A/I → Aggregates Area to Image Area Ratio

B → Matrix of variances between the mean of the classes

Comp → Compactness

Conv → Convexity

CR → Concavity Ratio

D → Fractal dimension AFDD → Area vs. Feret Diameter Fractal Dimension APD → Area vs. Perimeter Fractal Dimension

DBM → Mass Fractal Dimension

DBS → Surface Fractal Dimension

DEDM → Euclidean Distance Map Fractal Dimension

DEq → Equivalent Diameter

Df → Slope of Log (Pλ) vs. Log (λ)

df → Discriminant function

jidf → Value of the discriminant function j for object i

kjdf → Average value of the discriminant function j in class k

DMR → Mass Ratio Fractal Dimension PFDD → Perimeter vs. Feret Diameter Fractal Dimension

E → Error

e → Eigenvector

List of Symbols


Eij → Error for the line i and column j

vardfe → Coefficient of the original var variable

er → Number of erosions

erComp → Number of erosions needed to delete the complement of an object

erObj → Number of erosions needed to delete an object p

gErr → Error of the g neuron for an example p

ERSMax → Maximum number of erosions

Ext → Extent

f → Input function

fa → Activation function

FA % → Fines Area Percentage

FCal → Calibration factor

FD → Feret Diameter

fDil → Dilution factor

Fk → Classification value of each object in class k

FMax → Length (Maximum Feret Diameter)

FMax90 → Feret Diameter at 90º of the Maximum Feret Diameter

FMin → Minimum Feret Diameter

fo → Output function p

ogf → Obtained output value

FrShp → Feret Shape

FW % → Fines Weight Percentage

f1 → First degree of freedom

f2 → Second degree of freedom

g → Neuron

GR → Gyration Radius

h → Node

0Dh → Settled Diluted Sludge Height after 0 minutes of settling time

30Dh → Settled Diluted Sludge Height after 0 minutes of settling time

h0 → Settled Sludge Height after 0 minutes of settling time

h30 → Settled Sludge Height after 30 minutes of settling time

I → Unity matrix

J → Box size

K → Constant

List of Symbols


k → Class

kTotal → Total number of classes

L → Filament Length

LBB → Bounding Box Length

lrate → Learning rule

LStk → Stalk Length

m → Intensity level

m’ → New m intensity level value

mx → Number of columns in matrix X

my → Number of columns in matrix Y

M1X → First order horizontal moment

M1Y → First order vertical moment

M2X → Second order horizontal moment

M2XY → Second horizontal and vertical moment

M2Y → Second order vertical moment

N → Number of pixels in an image

Nb → Aggregates Number

NbClass → Class Total Number

Nb % → Aggregates Number Percentage

ndf → Number of discriminant functions

nFines → Total number of Fines aggregates

Ni → Number of intensity levels in an image

NInt → Number of filament intersections

NJ → Number of J sized boxes of an object

nk → Number of objects in class k

Nm → Number of m intensity level pixels in an image

NObj → Pixel sum of each object

nObj → Total number of objects

NPer → Pixel sum of the objects boundary

NThn → Pixel sum of each thinned filament

nvar → Number of original variables

P → Perimeter

p → Example

PConv → Convex Envelope Perimeter

PF → Perimeter Factor

List of Symbols


PJ → Number of J sized boxes crossing the perimeter of an object

pm → Probability for a pixel to have an m intensity level

PP → Deleted Stalk Perimeter

PRESS → Predicted residual error sum of squares

PStk → Stalk Perimeter

Pλ → Perimeter for each λ Euclidean distance

q → Quantile

QF → Feed Flow Rate

QR → Recycle Flow Rate

r → Radius

Rec. % → Area Recognition Percentage

Rob → Robustness

RTer → Number of erosions needed to remove the stalk

S → Section Area

ShF → Shape Factor

Sol → Solidity

SVID → Diluted Sludge Volume Index

T → Matrix of ti latent variables

t → Iteration

TA → Aggregates Total Area

TAClass → Class Total Area

ti → Latent variable for PLS modelling

TL → Total Filaments Length

TN → Aggregates Total Number

TNClass → Class Total Number

TW → Total Aggregates Weight

U → Matrix of ui latent variables

u → Summation function

ui → Latent variable for PLS modelling

V → Volume dfival → Value of the df discriminant function for the object i

var → Original variable

W → Matrix of pooled within-classes variance of all classes

WA → Average Width

WB → Body Width

List of Symbols


WBA → Body Average Width

WBAWB → Body Average Width per Body Width Ratio

WBB → Bounding Box Width

whg → Optimal connection weights

Wi → Weight of the i aggregate

wi → Weights

WStk → Stalk Average Width

WSWBA → Stalk Average Width per Body Average Width Ratio

w0 → Region of an image with a grey level equal or less than j

w1 → Region of an image with a grey level higher than j

X → Matrix of independent data

x → Vector of mean values from all objects

xi → Input connections

xij → Elements of the X matrix of independent data inx → Horizontal coordinate of each object pixel

kx → Vector of the mean values for each class

knx → Value of object n for class k

xvari → Value of the original var variable for the object i

Y → Matrix of dependent data

y → Predicted values

yij → Elements of the Y matrix of dependent data iny → Vertical coordinate of each object pixel

pgy → Desired output value

α → Error probability

γ → Eigenvalues

∆whg → Weight change

λ → Euclidean distance

φ → Entropy

List of Symbols


List of Abbreviations


LIST OF ABBREVIATIONS ASCII → American standard code for information interchange

BMP → Bit-mapped format

BOD → Biochemical oxygen demand

BOD5 → Biochemical oxygen demand measured after 5 days

C → Carbon

CCD → Charge-coupled device

CID → Charge-injected device

CMD → Charge modulated device

CMOS → Complementary metal oxide silicon

CMYK → Cyan, magenta, yellow and black

CNRS → Centre nationale de la récherche scientifique

COD → Chemical oxygen demand

DA → Discriminant analysis

DNA → Deoxyribonucleic acid

DO → Dissolved oxygen

EDM → Euclidean distance map

EGSB → Expanded granular sludge blanket

ENSIC → École nationale supérieure des industries chimiques

F:M → Food-to-micro-organisms ratio

GIF → Graphical interchange format

HRT → Hydraulic Retention Time

HSI → Hue, saturation and intensity

ICM → Iterated conditional modes

INPL → Institut national polytechnique de Lorraine

JPEG → Joint photographer’s expert group

LCFA → Long chain fatty acids

LoG → Laplacian-of-Gaussian

LSGC → Laboratoire des sciences du génie chimique

LUT → Look-up table

MLSS → Mixed liquor suspended solids

MPEG → Moving pictures expert group

MPL → Multilayer perceptrons

List of Abbreviations


N → Nitrogen

NN → Neural Networks

OHPA → Obligate hydrogen producing acetogens

OLR → Organic Loading Rate

P → Phosphorous

PCA → Principal components analysis

PLS → Partial Least Squares

RGB → Red, green and blue

ROI → Region of interest

S → Sulphur

SBI → Sludge biotic index

SVI → Sludge Volume Index

TIFF → Tagged image file format

TOC → Total organic carbon

TS → Total solids

TSS → Total Suspended Solids

TXT → Text file

UASB → Up-flow anaerobic sludge blanket

VSS → Volatile Suspended Solids

31

1 GENERAL INTRODUCTION

32

1.1 Image Processing and Analysis in Biotechnology 33 1.1.1 Short review on image analysis applications 34 1.1.2 Fundaments of Image capture, processing and analysis 37 1.2 Brief Introduction to Wastewater Treatment 41 1.3 Aerobic Wastewater Treatment Processes 45 1.3.1 The activated sludge wastewater treatment process 46 1.3.1.1 The Activated Sludge Process Basis 46 1.3.1.2 The Activated Sludge Floc Structure 49 1.3.1.3 Perspective and Aim of Work 52 1.3.2 Protozoa and metazoa in activated sludge 53 1.3.2.1 Protozoa and Metazoa Population Dynamics 53 1.3.2.2 Protozoa and Metazoa Systematic 55 1.3.2.3 Perspective and Aim of Work 57 1.4 Anaerobic Wastewater Treatment Processes 61 1.4.1.1 Anaerobic Treatment Basis and Technology 62 1.4.1.2 The Granulation Process 65 1.4.1.3 Granule Deterioration 67 1.4.1.4 Perspective and Aim of Work 67

General Introduction Image Processing and Analysis in Biotechnology 1.1


1.1 IMAGE PROCESSING AND ANALYSIS IN BIOTECHNOLOGY

In this section a short bibliographic review on the main application fields of image analysis specifically in biotechnology is reported. Furthermore, a brief introduction to image acquisition, processing and analysis techniques is presented.

The key applications of image analysis nowadays are first referred and then centred on biotechnology applications and finally on wastewater treatment sciences. The main works within this area as well as a bibliographic review are provided.

Main image acquisition systems and image processing and analysis techniques are referred also in this section. They are presented in a straightforward manner from the early image capturing, storing, pre-processing, enhancement, segmentation, post-processing and analysis.

General Introduction 1.1 Image Processing and Analysis in Biotechnology


1.1.1 SHORT REVIEW ON IMAGE ANALYSIS APPLICATIONS

Image processing and analysis or shortly image analysis has become nowadays a very important tool with a large field of applications. The image analysis systems strength resides on the ability to remove the subjectiveness of human analysis, the possibility to extract quantitative data that would be impossible or very difficult to obtain by other means and avoid tedious and highly time-consuming tasks to human researchers. Furthermore, with the exponential increase in computer processing capabilities and affordability as well as better imaging systems, image analysis has become a standard routine in many day-to-day applications and scientific studies.

Some of the more important applications of image analysis reside on the following fields (Glasbey and Horgan, 1995): medical imaging such as in the analysis of magnetic resonance images (Fowler et al., 1990), X-ray computer tomography and ultrasound images (Simm, 1992); geographical data such as in the analysis and creation of landsat thematic maps and synthetic aperture radar based maps (Horgan, 1994); textile industry such as in cashemere fibres studies (Russel, 1991); biology and biotechnology such as in the study of algal cells (Martin and Fallowfield, 1989), muscle fibres (Maltin et al., 1989), plant-cells embryo (Pons and Vivier, 1998), electrophoretograms images (Horgan et al., 1992) and in DNA sequencing by gel autoradiograph images.

Also in many cellular technologies studies image analysis techniques are a well-established complement of macro and microscopic visualization techniques allowing for a non-subjective qualitative and quantitative analysis of micro-organisms. Most common applications reside on the study of the morphology of fungal hyphae (Ritz and Crawford, 1990, Patankar et al., 1993), yeasts characterisation (Pons et al., 1993), enumeration and sizing of aquatic bacteria (David and Paul, 1989), cell sensitivity and antibiotic screening (Hammonds and Adenwala, 1990), colony texture analysis (Pons et al., 1995), biomass determination (Pons and Vivier, 1999), fungus colonies biochemical and mycelium differentiation (Morrin and Ward, 1989), microbial adhesion (Pons et al., 1992), and motility Amaral (1998b), among several other that can be found more detailed in Vecht-Lifschitz and Ison (1992).

Therefore, there comes as no surprise that, the application of image analysis procedures to complement the well established wastewater treatment operating parameters measurements is ever growing. Both the aerobic and anaerobic processes should be of interest of this novel technique, mainly on the morphological characterization of the microbial aggregates (flocs, pellets, granules) and in the evaluation of filamentous bacteria contents. Therefore, the determination of these aggregates and filaments parameters is among the most valuable information that image analysis systems can offer to this field of application. In aerobic processes it is also known that the knowledge of the protozoa and metazoa species present on the aerated tanks provides valuable information on the plants operating conditions. In fact, efforts should be made to automatically identify, by means of image analysis, the most common wastewater treatment protozoa and metazoa species.

The literature about the application of image analysis procedures in this field has been growing quite rapidly in the past few years. Hence, some authors have already tested automated image analysis methods to characterise the aerobic flocs morphology such as the works on the best image acquisition procedures of Desroches et al. (2001) to allow the establishment of a rigorous acquisition procedure. Nowadays, there are some



works relating the activated sludge morphology with the settleability properties such as the ones of Ganczarczyk (1994) on the relation between the settling velocity and the Length, Width and Equivalent Diameter of the aggregates, or the works of Grijspeerdt and Verstraete (1997) which used morphological parameters such as Form Factor, Roundness and Equivalent Diameter as well as fractal dimensions and correlated them to operating parameters such as the Sludge Volume Index (SVI) and the settling velocity. In more recent works the Equivalent Diameter, fractal dimensions and filamentous bacteria contents were determined in the works of da Motta et al. (1999, 2000, 2001a) and related to the SVI, and in the study of da Motta (2001) also to the settling velocity, subsequently used to monitor bulking events in wastewater treatment plants. Another approach regarding the filamentous bacteria was set by Heine et al. (2001) that determined the filament fraction of the biomass as the ratio between the filaments and flocs area. Amaral et al. (2002) related this later parameter as well as the filaments and aggregates contents and six other morphological parameters to the Total Suspended Solids (TSS) and SVI by a Partial Least Squares (PLS) multivariate statistical technique. Several related works are also of great importance such as the ones of Cenens et al. (2001, 2002) on threshold limit determination and distinction between aggregates and filaments from grey-scale images and subsequent segmentation.

Despite their potential, up to date few have been the studies about automatic image analysis morphological characterization of protozoa and metazoa. The use of morphological parameters and fractal dimensions coupled to multivariable statistical techniques such as Principal Component Analysis (PCA), to identify the protozoa and metazoa are referred in the works of Amaral et al. (1998, 1999, 1999a), and Baptiste et al. (1998). A different yet related, multivariate statistical technique called Discriminant Analysis (DA) was used with the same purpose in the studies of da Motta et al. (2001b, 2001c, 2001d) and Amaral et al. (2001). Different approaches however can be of interest as it is referred in the studies on protozoa characterization by pattern recognition techniques used by Golz et al. (2001). One should keep in mind, though, that these are pioneer studies and therefore, new methodologies can and most certainly will be implemented, however these works are among the first and promising on this subject.

The microbial aggregates morphology changes occurring in anaerobic granulation and granule-deterioration processes are also likely to be monitored by image analysis methodologies. Already there are some works aiming at the survey of the morphological changes in the anaerobic aggregates such as the ones of Dudley et al. (1993), Jeison and Chamy (1998) and Singh et al. (1998) which determined morphological parameters such as the number and aggregates size. A few pioneer studies on the distinction between floccular and granular structures by the use of fractal dimensions were published by Bellouti et al. (1997) and Alves et al. (1995) and were carried on to the monitoring of anaerobic digester aggregates by both fractal dimensions and morphological parameters such as in the works of Amaral et al. (1997). Furthermore, these studies led also to the filamentous bacteria contents assessment as presented in the works of Amaral et al. (2001a), Alves et al. (2000, 2000a) and Araya-Kroff et al. (2002). Different as they are, all these last approaches studied the outer silhouette of the aggregates whereas the works of Howgrave-Graham and Wallis (1993) focused on the inner nature of the aggregates by the use of Transmission Electron Microscope (TEM) images. Based on the auto-fluorescence capabilities of methanogenic bacteria other approaches have been attempted consisting on the assessment of aggregates fluorescence such as reported in Amaral (1998b), Amaral et al. (1998a) and Ahn et al. (2000) and its relation to the methanogenic bacteria contents.



In conclusion, although quite recent, image analysis methods are being progressively more implemented in day-to-day procedures covering a great variety of different areas ranging from medical imaging to geographical data or from textile industry to biology and biotechnology studies. Concerning this last subject and more specifically wastewater treatment processes applications there is also a clear trend towards their widespread use with more and more works on this field being currently published.



1.1.2 FUNDAMENTS OF IMAGE CAPTURE, PROCESSING AND ANALYSIS

Image analysis, in a wide sense, refers both to the strictly image analysis processes as well as the previous processes of image capture and image processing (Dougherty, 1994). Most used image capture methods employ analogue or digital video cameras attached to stereo, optic or electronic microscopes, laser scanning densitometers, as well as standard video capture and even digitalization scanners. With the exception of this later one the images are acquired to the computer by a frame grabber card.

Regarding the image acquisition hardware several different type of cameras are available. Older cameras rely on vidicon tubes and are designated as tube-type cameras, whereas newer cameras based on CCD (Charge-Coupled Device), CID (Charge-Injection Device), CMOS (Complementary Metal Oxide Silicon) or CMD (Charge Modulated Device) detectors are denominated solid state cameras (Pons and Vivier, 1999). The most widely used CCD analogue cameras have a matrix of photosensitive elements (sensors) that, during the so-called accumulation phase, acquire an electrical charge proportional to the number of absorbed photons. These electric charges are subsequently transported trough the chip from sensor to sensor and ultimately into voltage differences at the end (Jähne, 1995). The fundamental characteristics in the choice of a video camera are the spatial resolution, light sensitivity and the signal to noise ratio (Pons and Vivier, 1999).

The frame grabber card then receives the analogue camera output signal in terms of voltage differences and transforms it into digital information that can be used by the computer. This process resides on the transformation of each CCD sensor voltage differences to a value equal to the mean value of each sensor by a Look-Up Table (LUT), which finally leads to the formation of a matrix of picture elements (called pixels) directly proportional to the light intensity received by each sensor. The number of bits allocated to the pixels of any given image determines the number of colours in the image. For greyscale images (still the most widely used) it is common to represent each pixel in 8 bits corresponding to 256 grey levels. In colour images the standard acquisition formats are the RGB (Red, Green and Blue channels) the CMYK (Cyan, Magenta, Yellow and blacK channels) and the HSI (Hue, Saturation and Intensity). The most widely used RGB format, takes a total of 24 bits (True Colour) for each pixel with 8 bits assigned to each colour channel.

Once an image is grabbed, that is acquired, to the computer it needs to be stored. A standard 512x512 pixel sized colour image with 24 bits occupies a reasonable amount of space (around 768 Kbytes) in the disk hence, for general applications these images are stored in compressed formats such as the CompuServe Bitmap format (GIF) or the Joint Photographers Expert Group format (JPEG) for single images and the Moving Pictures Expert Group format (MPEG and MPEG2) for video sequences. In image processing and analysis however this, although apparently small, loss of information is of critical importance and, therefore, not acceptable, leading to the use of the larger Tagged Image File format (TIFF) or Windows Bitmap format (BMP).

Subsequently to acquisition, images are then processed in order to obtain a final image, grey scaled or binary, containing the information required for a given application. The first step in any image processing method should focus on the determination and removal of the background or background light differences. Two of the main approaches



reside on either dividing the image by a background image or in the subtraction of the background image. The background image itself must be obtained a priori upon the acquisition of the images or obtained a posteriori from the image(s) by a series of grey-scale closing and opening operations.

Next, image random noise should be eliminated or, at least, attenuated and, for this purpose, a series of smoothing filters either linear or non-linear can be applied. In linear filters the output values are linear combinations of the original values in the image such as the moving average filter, the Gaussian filter or frequency domain lowpass filters as the Wiener filter. In opposition to linear filters, in non-linear filters the output values are non-linear combinations of the original values in the image such as histogram based filters like the moving median filter and trimmed mean filter or spatially adaptative filters like Lee’s filter or the minimum variance filter (Glasbey and Horgan, 1995). If the nature of the noise is not random but periodic, such as electronic noise, this can be identified and eliminated by the use of Fourier transforms or fast Fourier transforms (Jähne, 1995).

After the image noise has been dealt with, the objects or regions of interest (ROI) should be enhanced to make possible their recognition by either the user or automatic segmentation algorithms. This purpose can be achieved by the use of linear filters for contrast enhancement such as normalisation, histogram equalisation or the use of logarithmic or exponential functions on the image histogram (Russ, 1995). Alternatively a series of greyscale morphological operations can be implemented such as grey scale erosions and dilations, top and bottom hat filters or even Mexican hat filters. In order to further strengthen details or boundaries in images several well known linear or non–linear high frequencies filters can be used. Examples of highpass linear filters are both the first derivative filters and the second derivative filters like the Laplacian filter, Laplacian-of-Gaussian (LoG) filter or the unsharp masking filter. Highpass non-linear type of filters include simple edge filters like the range filter, Robert’s filter or Kirsch filter as well as gradient filters like Prewitt’s filter, Sobel’s filter and Canny filter (Glasbey and Horgan, 1995).

In purely image processing applications the resulting image of the last step can be considered as the final image. However, such is not the case in image analysis applications where a segmentation step is further required and is of critical importance. In order for the segmentation to take place, a threshold value or values must be defined to allow the differentiation between the objects and the background. There are several ways in which this value or values can be obtained ranging from manual input values to fully automatic procedures comprehending three different approaches: thresholding, edge-based segmentation and region-based segmentation. In thresholding the pixels are individually segmented accordingly to their value and the main techniques cover histogram-based thresholding methods, multivariate classifiers methods and contextual classifiers methods. Histogram-based thresholding methods include the mean value, median value, histogram first derivative minimum, histogram first derivative maximum and histogram inflection point values (Cenens et al., 2001), intermeans algorithm, minimum error algorithm and the Otsu algorithm (Otsu, 1979). Multivariate classifiers methods include linear discrimination and K-means clustering whereas contextual classifiers methods include post classification smoothing and the ICM algorithm. Edge-based segmentation requires the previous use of an edge filter in order to segment edge from non-edge pixels and comprehends methods like the connected components algorithm or the LoG filter. Finally, the region-based segmentation works iteratively by grouping together neighbour pixels with similar values and cover methods like the



watershed algorithm, entropy algorithms and the split-and-merge algorithm (Glasbey and Horgan, 1995).

After the determination of the threshold value or values the image can be segmented in two, three or more parametric regions. In all cases the background region usually is set to zero with the segmented objects taking the value of one in two regions segmentation (binary image with white objects in a black background) or constant integer values in three or more regions segmentation leading to a final labelled image. A final situation may occur when only the background region is set to zero and the objects values remain untouched which can be seen as the filtering of a greyscale image with a binary mask image.

A post-processing of the binary or mask images may be, at times, needed in order to resolve some problems like the removal of border objects (cut-off by the borders of the image), removal of debris or the separation of touching objects among several others. The operations used to this purpose cover shape and size operations, connectivity operations and texture operations. Shape and size operations include morphological operations such as erosion, dilation, opening, closing and distance transforms whereas connectivity operations include skeletonisation, thinning, thickening, pruning and watershed and other advanced algorithms and finally texture operations rely on entropy based evaluations like the auto-crossproduct (Glasbey and Horgan, 1995).

Image analysis, strictly defined, is carried out once the final images are obtained. The type of measurements determined during image analysis depends on the type of final image, either binary, label or greyscale image and on the required data. In binary images the most common morphological parameters are the Area, Equivalent diameter, Perimeter, Length, Width, Eccentricity, Shape factor, Roundness, Extent, Convexity, Compactness and Solidity as well as the fractal dimensions like Mass fractal dimension, Surface fractal dimension, Mass ratio fractal dimension, Area vs. Perimeter fractal dimension, Area vs. Feret diameter fractal dimension, Perimeter vs. Feret diameter fractal dimension and Euclidean distance map fractal dimension among several others. In greyscale images apart from the ones described above other measurements can be obtained such as first, second and third massic moments and minimum, average, maximum and standard deviation greyscale values. Image analysis can also be performed to image sequences in order to determine, among several others, mobility parameters. In this particular type of images the determination of the velocity and direction of movement as well as tumbling frequency and magnitude are of the most common parameters.



General Introduction Brief Introduction to Wastewater Treatment 1.2


1.2 BRIEF INTRODUCTION TO WASTEWATER TREATMENT

In this section a brief introduction to wastewater treatment processes importance and worldwide application is provided.

Furthermore, the sewage composition is discussed with a special focus on domestic sewage as well as the most widely studied wastewaters operating parameters.

The wastewater treatment processes objectives and main steps are also referred in this section and a comparison between aerobic and anaerobic treatment scenarios is discussed.

General Introduction 1.2 Brief Introduction to Wastewater Treatment


In England, by the middle of the nineteenth century, waterborne diseases were uncontrolled and several epidemics resulted in thousands of victims. As an increased knowledge on the role of the micro-organisms in diseases aroused, so did a higher demand on water and wastewater treatment, which finally led to the creation of the first wastewater treatment plants (Bitton, 1994). Nowadays wastewater treatment facilities are widespread all over the world treating both domestic and industrial wastes. Domestic sewage and food industry wastes contain mainly non-toxic substances, whereas toxicants may be found in the wastes of industries such as coal processing (phenolics, ammonia, cyanide), petrochemical (oil, petrochemicals, surfactants), pesticide, pharmaceuticals and electroplating (copper, cadmium, nickel, zinc) (Kumaran and Shivaraman, 1988).

Domestic wastewater is a combination of human and animal excretions and washing, bathing and cooking grey water. The main components are amino-acids, peptides and proteins (40%-60%), carbohydrates (25%-50%), volatile and fatty acids and esters (10%), urea and a large number of trace organics such as pesticides, surfactants, phenols and priority pollutants (metals, non-metals, benzene and chlorinated compounds resulting in a Carbon-to-Nitrogen-to-Phosphorous (C:N:P) ratio of 100:5:1 (Bitton, 1994, Tchobanoglous and Burton, 1991)). Typical values for the characterization of a domestic wastewater are described in Table 1.1 (adapted from Tchobanoglous and Burton, 1991).

The main parameters used to for the determination of both organic and inorganic matter in wastewaters are as follows (Bitton, 1994):

Biochemical Oxygen Demand (BOD) → Represents the amount of dissolved oxygen (DO) consumed by the micro-organisms for the biochemical oxidation of organic and inorganic matter.

Chemical Oxygen Demand (COD) → Represents the amount of oxygen necessary to oxidize the organic carbon completely to carbon dioxide, water and ammonia (Sawyer and McCarty, 1967).

Total Organic Carbon (TOC) → Represents the total organic carbon in a given sample and is independent of the oxidation state of the organic matter.

Total Solids (TS) → Represents the amount of organic and mineral solids, comprising both suspended and dissolved solids.

Total Suspended Solids (TSS) → Represents the amount of organic and mineral suspended solids, including micro-organisms.

Volatile Suspended Solids (VSS) → Represents the organic portion of TSS, which comprises non-microbial organic matter, dead and live micro-organisms and cellular debris (Nelson and Lawrence, 1980).

Food-to-Micro-organisms Ratio (F:M) → Represents the organic load per suspended solids in a given period of time.

The major contaminants found in wastewater are volatile (biodegradable or recalcitrant) organic compounds, suspended solids, nutrients (nitrogen and phosphorous), microbial pathogens and toxic metals. Hence, the main objectives of waste treatment processes are:

Wastewater organic content reduction → Includes the removal of toxic or carcinogenic recalcitrant trace organics.

Nutrients removal or reduction → Removal of nitrogen and phosphorous nutrients to reduce the pollution of receiving surface waters or ground waters.

Pathogenic micro-organisms removal or inactivation.

General Introduction Brief Introduction to Wastewater Treatment 1.2


Table 1.1 – Typical characteristics of domestic wastewater (adapted from Tchobanoglous and Burton, 1991).

Concentration Parameter (mg/L) Strong Medium Weak

BOD5 400 220 110 COD 1000 500 250

Total Nitrogen 85 40 20 Total Phosphorous 15 8 4

Total Solids 1200 720 350 Suspended Solids 350 220 100

Wastewater treatment is generally accomplished by the use of physical, chemical and biological processes. Treatment methods relying on physical processes are called unit operations and include screening, sedimentation, filtration, and flotation. Treatment methods based on chemical or biological processes are called unit processes. Among the chemical processes are the disinfection, adsorption and precipitation, whereas biological processes involve microbial activity responsible for the degradation of organic matter and removal of nutrients (Tchobanoglous and Burton, 1991). The choice between one of the above must be carried out on the basis of effluent type, biodegradability, toxics presence and sludge production among others. Due to the operating costs, as well as simplicity, the biological process is nowadays preferred for the treatment of urban and some types of industrial wastewaters.

Wastewater treatment processes may comprise the following steps: Preliminary treatment → The objective of this operation resides on the

removal of debris and coarse materials such as bone chips and glass that may clog the plant’s equipment (pipes and pumps).

Primary treatment → Unit operations such as screening and sedimentation compose this stage.

Secondary treatment → During this stage nutrient removal is performed by unit processes such as biological (e.g., activated sludge, trickling filter, and oxidation ponds) or chemical (e.g., disinfection) processes.

Tertiary (advanced) treatment → Unit operations and chemical unit processes are used to further reduce BOD, nutrients, pathogens and toxic substances.

Both aerobic processes and anaerobic digestion are significantly used in the treatment of both domestic and industrial wastewaters. The main advantages of anaerobic processes over aerobic ones reside on the following (Lettinga et al., 1980; Sahm, 1984; Sterrit and Lester, 1988; Switzenbaum, 1983):

Anaerobic digestion uses readily available carbon dioxide as an electron acceptor thus, requiring no oxygen and being less expensive.

Anaerobic treatment produces lower amounts of sludge (3 to 20 times less than aerobic processes) and, most of the energy derived from the substrate breakdown is found in the final product: methane. Whereas 50% of the organic carbon is converted into biomass in aerobic processes, in anaerobic conditions that value reaches only 5%.

General Introduction 1.2 Brief Introduction to Wastewater Treatment


Anaerobic biomass is capable of retaining its activity even after long periods of starvation.

Anaerobic digestion produces methane, a highly energetic gas which can be burn on site to produce heat for the digesters or to generate electricity.

Energy requirements for anaerobic wastewater treatment are reduced. Anaerobic digestion is suitable for high loading rates, xenobiotic chlorinated

aliphatic hydrocarbons and recalcitrant natural compounds.

However there are also a few disadvantages in anaerobic processes to be aware of: It is a slower process than aerobic digestion. Anaerobic processes possess an increased sensitivity to upsets by toxicants. Longer periods of time are required for the start-up of the process. Anaerobic processes require high concentrations of primary substrates in order

to degrade xenobiotic compounds, by co-metabolism.

The lowest substrate affinity of the anaerobic digestion hinders its application as a unique wastewater treatment approach. Therefore, a combination of both aerobic and anaerobic treatment processes is, nowadays, regarded as the most effective method to treat domestic as well as industrial effluents. A comparison of the operating conditions of both wastewater treatment processes is shown in Table 1.2 (adapted from Brouzes, 1973).

Table 1.2 – Comparison of the operating conditions between aerobic and anaerobic treatment processes (adapted from Brouzes, 1973).

Aerobic Treatment Anaerobic Treatment Sludge age 1–5 days 10–30 days

Oxygen uptake 0.5–2 Kg O2 / kg BOD5 None

Gas production Carbon dioxide Carbon dioxide, methane and molecular hydrogen

Important controls Dissolved oxygen (DO) Volatile fatty acids and pH

Heating Not necessary above 5ºC 35ºC for mesophile strains and 55ºC for termophile strains

After the wastewater treatment has taken place, there is a need to deal with the resulting sludge, mainly composed by of solids generated during the previous processes. Prior to the sludge treatment and disposal, the sludge has to be first thickened in settling tanks, so that the solid concentration is increased, then dewatered either by filtration or its application on drying beds, conditioned by chemical or heat treatment and then, finally stabilized. The stabilization process aims at breaking down the organic fraction of the sludge in order to reduce its mass and obtain a safer and less odorous product (Bitton, 1994). Sludge stabilization can be performed by anaerobic or aerobic digestion, composting, lime stabilization and heat treatment (Tchobanoglous and Burton, 1991).

General Introduction Aerobic Wastewater Treatment Processes 1.3


1.3 AEROBIC WASTEWATER TREATMENT PROCESSES

In this section, the aerobic wastewater treatment process is discussed both from the viewpoint of the activated sludge wastewater treatment process as from the aerated tanks protozoa and metazoa viewpoint.

Regarding the activated sludge wastewater treatment process the main plant operating parameters are introduced as well as the plant most common micro-fauna and their trophic relationships. An analysis on the activated sludge floc structure is presented based on the enlightenment of the micro-organisms aggregation process, the resulting floc morphology and its relationship with bulking problems. Finally, the objectives of this activated sludge study by image analysis methods are explained.

The most common protozoa and metazoa in activated sludge are also discussed in this section mainly the protozoa and metazoa population dynamics in the aerated tank such as the colonization process and their relation with process parameters and performance. Protozoa and metazoa systematic is also disclosed in this section with a clear emphasis to protozoa. Finally, the objectives of this protozoa and metazoa study by image analysis methods are explained.

General Introduction 1.3 Aerobic Wastewater Treatment Processes


1.3.1 THE ACTIVATED SLUDGE WASTEWATER TREATMENT PROCESS 1.3.1.1 THE ACTIVATED SLUDGE PROCESS BASIS

Among the different secondary treatment biological processes, the most widely used is the activated sludge system, consisting of inoculating a high filamentous and floc-forming bacteria concentration that will be responsible for the oxidation of the organic matter in an aerated tank. Subsequently, the flocculated biomass is separated by means of their settling ability from the treated effluent in a so-called clarifier or settling tank. Part of the settled biomass is then returned to the aerated tank in order to maintain a constant biomass concentration. The activated sludge process is summarized, accordingly to Canler et al. (1999) in Figure 1.1.

Figure 1.1 – Activated sludge wastewater treatment process (adapted from Canler et al., 1999).

In the aerated tank, the primary effluent is brought in and mixed with recycled sludge to form the mixed liquor, containing about 1500 to 2500 mg/L suspended solids (Bitton, 1994), with the aeration (minimum of 2 mg O2/L) provided by mechanical methods. An important characteristic of the activated sludge process is the recycling of a large proportion (above 95%) of the activated sludge, which causes a much higher mean cell residence time (sludge age) than the Hydraulic Retention Time (HRT) (Sterrit and Lester, 1988). This procedure helps maintaining a high number of micro-organisms oxidizing the organic compounds in a short period of time. Indeed, the retention time in the aerated tank can vary from 4 to 8 hours, whereas sludge age ranges from 5 up to 15 days (Bitton, 1994). The settling tank is used for the sedimentation of the microbial aggregates (flocs) produced in the aerated tank. A portion of this sludge is then recycled back to the aerated tank in order to maintain a low food-to-micro-organisms ratio (F:M) between 0.2 to 0.5 Kg BOD5/(Kg TSS x day) according to Bitton (1994). Such a low F:M ratio keeps the micro-organisms in the aerated tank starved and therefore, leads to a more efficient wastewater treatment in terms of BOD removal.

However, and in accordance to the organic load present in the aerated tank of the wastewater treatment plant, these values can vary, quite significantly as shown in Table 1.3 (adapted from Forster, 1977).

Settling Tank Effluent

Aerated Tank

Final Effluent

Sludge Purge Sludge Recirculation

Organic + Activated Waste Sludge

Activated Sludge +

CO2, H2O, NO3-

SO42-, PO43-



Table 1.3 – Values of the activated sludge treatment operating conditions for domestic sewage (adapted from Forster, 1977).

Low load Medium load High load (Kg BOD5/m3.day) 0.125–0.5 0.6–1.6 2–6 Organic load

(Kg BOD5/Kg TSS.day) 0.02–0.1 0.2–0.5 1.5–5 HRT (hours) > 12 2–4 2–3

Oxygen uptake (Kg O2/Kg BOD5) 1.5–2 0.5–1 0.3–0.5 Sludge production (Kg TSS/Kg BOD5) 0.15 0.55 0.75

Recycling rate > 0.95 0.85–0.95 0.6–0.75

The microbial cells aggregates or flocs settling abilities depend on their density, which in turn, depends on the F:M ratio and sludge age. It is known that good quality settling occurs when the sludge micro-organisms are in the endogenous phase, that is, when carbon and energy sources are limited, the microbial growth rate is low and therefore, the F:M ratio is low (Bitton, 1994). Conversely, a high F:M ratio (above 0.5 Kg BOD5/(Kg MLSS x day)) implicates a poor sludge settleability. Also, a mean cell residence time of at least 3 to 4 days is necessary for an effective settling accordingly to Tchobanoglous and Burton (1991), which reflects in Sludge Volume Index (SVI) values between 50 and 150 mL/g for MLSS content bellow 3500 mg/L where MLSS are the Mixed Liquor Suspended Solids. The Sludge Volume Index is the most used parameter to determine the settleability properties of the mixed liquor.

The activated sludge wastewater treatment depends primarily on an aerobic bacterial culture kept in suspension in an aerated tank feeding on the biodegradable organic matter of the raw effluent. The efficiency of this treatment, in terms of Biochemical Oxygen Demand (BOD) is around 50%, with the remaining BOD being transformed to carbon dioxide, water and ammonia. When the substrate becomes a limiting factor this culture can auto-oxidize, with the consequent decrease and mineralization of the biomass (Canler et al., 1999). However, activated sludge cannot be regarded as a bacterial solely culture, indeed quite the opposite as they are a microbiological enriched consortium of micro and macro-organisms. As a fact, the micro-fauna present in the activated sludge mixed liquor is composed of around 95% of bacteria and 5% of predator higher organisms such as protozoa, metazoa and higher invertebrates (Richard, 1989). Protozoa are unicellular animals varying from 5 up to 300 µm, while metazoa are multicellular animals with sizes that can surpass 1 mm (Jahn et al., 1999). The set of these micro-organisms represents the biological frame of the plant. Typical values of a stable state plant micro-organisms are briefly referred (Canler et al., 1999):

Bacteria (flocculated, filamentous and dispersed) → 109 per ml. Protozoa (flagellates, sarcodines and ciliates) → 104 per ml. Metazoa (rotifers, nematodes, etc) → 1-5 102 per ml.

Activated sludge biological processes are based on a micro-organisms population in constant competition for food. Decomposers growth (mainly heterotrophic bacteria) depends on the quality and availability of the effluent organic matter, while for the predators, the availability of prays dictates their growth. This cycle may be summarized as follows: Dispersed bacteria are grazed by both bacterivore flagellates and ciliates protozoa which are themselves pray of carnivorous organisms. These predation and competition relationships determine populations oscillation and succession until a dynamic stabilized state is achieved, dependent on plant operation parameters and type



of effluent (Madoni, 1994a). These complex trophic relationships of the mixed liquor micro-fauna (predation, competition and cannibalism) can be summarized by the diagram adapted from Canler et al. (1999) in Figure 1.2.

Figure 1.2 – Main trophic relationships in an aeration tank (adapted from Canler et al., 1999).

As above-mentioned, the biological reactor of a wastewater treatment plant by activated sludge is a complex ecosystem, composed of different types of bacteria (more than 300 species accordingly to Bitton (1994)), protozoa and metazoa, in charge of the degradation of the pollution. In the aerated tank there are two predominant classes of bacteria: Floc-forming bacteria which agglomerate as flocs, due mainly to exopolymers excretion and filamentous bacteria which are thought to constitute the floc backbone. A good balance between the different species is necessary for efficient pollution removal, good sludge settleability in the final clarifier and low suspended solids in the treated effluent. There are major physiological differences between floc-forming and filamentous bacteria as it is shown in Table 1.4 (adapted from Sykes, 1989). Filamentous bacteria have a higher surface-to-volume ratio than floc-forming bacteria and higher affinities for substrates which renders them more resistant to the lack of oxygen and nutrients and able to survive under starvation conditions. Therefore, filamentous bacteria predominate under low dissolved oxygen, low F:M ratios, low nutrient conditions and even high sulphide levels.

The main bacterial genera found in activated sludge flocs are Comanomonas-Pseudomonas (around 50%), Flavobacterium (around 14%), Paracoccus (around 12%), Alcaligenes and Coryneform (around 6%), Aeromonas, Bacillus, Micrococcus, Arthrobacter, Aureobacterium and fluorescent Pseudomonas (around 2%) and Zoogloea as well as filamentous micro-organisms (Bitton, 1994). Activated sludge flocs also haven autotrophic bacteria such as nitrifiers (Nitrosomonas and Nitrobacter) which convert ammonium to nitrate and phototrophic bacteria such as purple nonsulfur bacteria. Zoogloea are exopolysaccharide producing bacteria that are found in wastewater and other organically enriched environments. They are commonly found in various stages of the wastewater treatment although their number does not surpass 1% of the total bacteria in the mixed liquor (Williams and Unz, 1983). There are different types of filamentous bacteria: Microthrix parvicella are found generally in Europe (Pujol et al., 1994; Madoni et al., 2000; Eikelboom, et al., 1998; Wanner, et al., 1998 and Westlund, et al., 1996) whereas Nocardia

Bacteria

Producers

Protozoa

Metazoa

Predators

Substrate (Organic Matter)

Predator Pray



sp., Type 1701 (Richard, 1991; Madoni et al., 2000) and Gordona amarae-like organisms (Jenkins et al., 1993), prevail in North America.

Table 1.4 – Comparison of physiological characteristics of floc-forming and filamentous bacteria (adapted from Sykes, 1989).

Bacteria Characteristic Floc-Forming Filamentous

Maximum substrate uptake rate High Low Maximum specific growth rate High Low

Endogenous decay rate High Low Substrate importance in growth rate Significant Moderate

Resistance to starvation Low High DO importance in growth rate Significant Moderate Nitrate use as electron acceptor Yes No

Phosphorous uptake Abundant Not abundant

1.3.1.2 THE ACTIVATED SLUDGE FLOC STRUCTURE

It is believed that, the mixed liquor bacteria consortia agglomerate as flocs in the aerated tank, due to exopolymers that Zoogloea bacteria excrete and a filamentous bacteria backbone. This model, based on the first studies of Erikson and Härdin (1984) on the aggregation of the negatively charged surface bacteria, was seemingly revised by other authors. It is believed that the repulsion charges are weaker at the ends than at the side of the aggregates, promoting thus, an end-to-end flocculation of the individual flocs. Furthermore, each new flocculation step will be liable to increasingly elongate the floc structure, by the formation of new polymeric bounds between the flocs. This process ultimately gives raise to the formation of structurally weak elongated flocs in fresh sludge although, in the presence of high flow rates or aged sludge an increase in exopolymers production leads to structurally stronger rounded flocs. Jenkins et al. (1993) refers the ability of filamentous bacteria to create a filamentous network (backbone) allowing link formation between flocs. This model comprises two types of structure: a microstructure responsible for the microbial adherence, aggregation and flocculation processes composed by exopolymers producing zoogleal bacteria and a macrostructure formed by the filamentous bacteria backbone between the microstructures and the flocs. This latter structure originates stronger and larger irregular flocs which become rounder as the filamentous bacteria contents decrease. Moreover, Jorand et al. (1995) have described a model where they enumerate three levels of organization. The first level is formed by aggregates up to 2.5 µm in diameter connected to each other by polymeric bounds forming a second level of organization up to 13 µm in diameter. Exopolymer bounds tie this latter aggregates into a third level of organization up to 125 µm in diameter. Latter studies from Snidaro et al. (1997) on the fractal analysis of the two larger aggregates permitted to conclude that the mechanisms lying beneath the aggregation processes were quite different. Indeed, they have determined that cellular division was the formation mechanism within the 13 µm micro-colonies whereas on the 125 µm microflocs diffusion limited clustering mechanism was found.



Ideally an activated sludge floc should have a reasonable size and be composed by an adequate balance between floc-forming and filamentous bacteria. They should be quite robust and have good settling capabilities, hence leading to a low organic matter and low turbidity final effluent. The composition of an activated sludge floc, however, is far from being uniform, as the oxygen level in the flocs is diffusion-limited the number of active aerobic bacteria decreases as the floc size increases (Hanel, 1988, Wu et al., 1987).

Barbusinski and Koscielniak (1995) in accordance with the works of Chao and Keinath (1979) have observed the floc size increase with the increase of the organic load, mainly due to the higher production of exopolysaccharides. This larger flocs open structure, by turns, implies lower floc densities and compaction and consequent poorer settleability (Gregory, 1998). Barbusinski and Koscielniak (1995) have also observed that the maintenance of high organic loads for extended periods of time gave raise to pin point flocs bulking problems. Wilen and Balmer, (1999) have observed that high oxygen concentrations permitted large compact flocs, whereas low oxygen concentrations implicated excessive growth of filamentous bacteria leading to filamentous bulking problems and Sezgin (1982) related the number of filamentous bacteria with the Sludge Volume Index. As a matter of fact, the oxygen, nutrient and F:M ratio conditions within the aerated tank are of the utmost importance in bulking control. Furthermore, it is thought that low F:M ratio is the major cause of bulking problems in wastewater treatment plants.

The most common malfunctions found in activated sludge processes are as follows (Bitton, 1994):

Pinpoint flocs → This malfunction leads to the breakage of large flocs and the formation of small, compact and roughly spherical but structurally weak flocs due to low filamentous bacteria concentration in high organic loads. Although the larger flocs will easily settle, the smaller ones will barely settle in the clarifier (or not at all) leading to a final effluent with a low SVI (bellow 70 mg/L) but high turbidity and organic matter contents (Gerardi et al., 1990; Bitton, 1994).

Filamentous bulking → This malfunction is likely to happen in conditions such as the lack of specific nutrients as nitrogen and phosphorous, the presence of toxic substances and oxygen limitation. Filamentous bulking will lead to excessively large and linked flocs due to hydrophilic filamentous bacteria overgrowth, which extend far beyond the flocs limits, exceeding 10000 mm per mg of suspended solids (Sezgin, 1982). This bulking problem causes a poor compaction of the solids on the clarifier (high SVI values) as well as a strong reduction on the settling velocities. This is the most common malfunction in activated sludge processes.

Dispersed growth → In a well-operated activated sludge, dispersed bacteria are consumed by protozoa. However, in high BOD loads, oxygen limitation or the presence of toxic metals, floc-forming bacteria may not flocculate and therefore, the number of dispersed bacteria rapidly increases. This malfunction ultimately leads to a non-settling turbid effluent.

Zoogleal bulking (Viscous bulking) → This malfunction is usually associated with foaming problems (Novak et al., 1994) and highly biodegradable effluents in low oxygen and defined nutrients concentrations in the aerated tank (Al-Yousfi et al., 2000). Zoogleal bulking leads to a high viscous effluent (slime) due to the overproduction of exopolysaccharides. As a result of that, the flocs



will have poor settling abilities and compaction leading to a viscous and high organic contents final effluent.

Rising sludge → Rising sludge is the result of excess dinitrification, resulting from anoxic conditions in the settling tank, due to an excessive retention time in the settling tank. The sludge flocs attach to the denitrification produced nitrogen bubbles rising to the surface of the clarifier and forming, thus, a sludge blanket. As a result of that, the final effluent will be turbid and with high BOD values.

Foaming → This malfunction leads to the formation of a foam coat due to hydrophobic filamentous bacteria overgrowth (Duchène and Cotteux, 1998) like Nocardia sp. and sometimes Microthrix parvicella or nondegradable surfactants presence. These bacteria entrap small air bubbles lowering the flocs density and making them float, which finally leads to biomass loss due to overboard phenomena.

The floc morphology of the most common malfunctions found in activated sludge is shown in Figure 1.3.

Figure 1.3 – Floc morphology of the most common malfunctions found in activated sludge: a)

normal flocs. b) pin-point flocs. c) filamentous bulking. d) zoogleal bulking.

In order to overcome the above mentioned bulking problems it is common to use oxidants such as chlorinated compounds (10-20 mg/L), oxygen peroxide (100-200 mg/L) or even ozone (for filamentous bulking). Synthetic organic polymers, lime and iron salts may also be added to the mixed liquor to induce sludge settling, as well as cationic

(a) (b)

(d) (c)



polymers at concentrations of 15-20 mg/L. The control of operating parameters such as the sludge recirculation rate in the case of rising sludge and the F:M ratio, oxygen and substrate concentrations are also quite helpful in preventing the overgrowth of filamentous bacteria and thus, filamentous bulking. Also, it is possible to create a gradient inside the tank to favour either the growth of floc-forming or the filamentous bacteria (Bitton, 1994). Foam control can be achieved by the chlorination of foams or the return activated sludge, an increase in sludge wasting (since one of the causes of foaming is a long mean cell retention time), reducing air flow, pH, oil and grease levels and the addition of antifoam agents and iron salts among several techniques (Jenkins et al., 1993; Soddell and Seviour, 1990).

1.3.1.3 PERSPECTIVE AND AIM OF WORK

Classical methods to characterize aggregates and evaluate the contents in filamentous bacteria reside on manual counting under a microscope with an eyepiece with a micrometer. Sezgin et al. (1978) have developed a procedure to determine the filamentous bacteria total length, which was later used by Palm et al. (1980) and Lee et al. (1982), and allocate it into 8 distinct classes according to the filamentous bacteria abundance. Jenkins et al. (1993) have developed another technique to quantify Nocardia sp. Manual counting techniques are, however, rather tiring, imprecise and time-consuming methods which make them not feasible in wastewater treatment laboratories. Hence, some authors have tested automated image analysis methods to characterize the aggregate morphology, mainly in terms of their fractal dimension, and to relate it to settleability properties (Ganczarczyk, 1995; Grijspeerdt and Verstraete, 1996 and 1997). Indeed, automated image analysis seems an appropriate method to characterise quantitatively both aggregates and filamentous bacteria and, reliable information of this type should enable to improve the daily operation of wastewater treatment plants. Such a method has been proposed by da Motta et al. (1999) and Amaral et al (2002) and subsequently used to monitor bulking events in pilot plants (da Motta et al., 2000b).

The aims of the present work resides on the survey of an activated sludge treatment plant by monitoring the aggregates morphological parameters and the filaments content and relate them with operating parameters such as the Total Suspended Solids (TSS) and the Sludge Volume Index (SVI). For that purpose the multivariate statistical technique Partial Least Squares (PLS) was used in order to enlighten the relationships between the morphological parameters and the operating parameters.



1.3.2 PROTOZOA AND METAZOA IN ACTIVATED SLUDGE 1.3.2.1 PROTOZOA AND METAZOA POPULATION DYNAMICS

Numerous studies on the colonization of the aerated tanks by protozoa and metazoa reveal the key role of the plant operating conditions. As a matter of fact, in identical aerated tanks with similar organic load, aeration rate and sludge age the resulting protozoan community is almost identical (Madoni, 1994a). The most common protozoa and metazoa in a wastewater treatment plant are essentially aerobic and bacterivore (with exception of a few flagellates) and are represented by over 200 different species. The primordial role of these protozoa and metazoa populations consists on the elimination of coliform and pathogenic bacteria through predation, hence controlling bacterial growth and, in the process, allowing for a clarified effluent (Curds et al., 1968). Additionally, they contribute for biomass flocculation by mucus secretion and breakage of excessively large flocs in the course of their intra-floccular mobility (Richard, 1989; Finlay et al., 1988). Although most of the protozoa and metazoa are bacterivore, some species can directly assimilate organic matter (a few flagellates) or predate other protozoa. In most of the bacterivore cases, bacteria should be present in the interstitial medium but, some protozoa and metazoa have the ability of nourishing inside the flocs (Canler et al., 1999).

Different protozoa and metazoa populations develop in the mixed liquor accordingly to the operating conditions. For instance, food availability will be decisive on the dominant group(s). Flagellates, sarcodines and small free-swimming ciliates require a higher amount of bacteria (higher than 106 or 107 per ml) due to their inefficient food capture ability. During the plant start-up, when there is a low Hydraulic Residence Time (HRT) and a high food to micro-organisms (F:M) ratio, these protozoa dominate. On the opposite, sessile ciliates and metazoa increase when there is a high HRT and a low F:M ratio due to their ability of floc adhesion or to their more efficient food capture mechanism (Richard, 1989). So, protozoa and metazoa populations are quite dependent on the sludge age which is dependent of the plant organic load as illustrated on Table 1.5 (Canler et al., 1999):

Table 1.5 – Relationship between sludge age and organic load (adapted from Canler et al., 1999).

Organic Load (Kg BOD5 / Kg VSS.day) Sludge age

Very high load > 1 Few hours High load ≈ 1 Few hours up to 1 day

Medium load < 0.5 Few days Low load < 0.2 Higher than 10 days Aerated < 0.1 Higher than 20 days

Generally the microbial population evolution in a wastewater treatment plant can be summarized by Figure 1.4 (Canler et al., 1999):



Figure 1.4 – Microbial population as a function of sludge age (adapted from Canler et al., 1999). (1 - Bacteria; 2 - Zooflagellates; 3 - Free-swimming and crawling ciliates; 4 - Sessile ciliates; 5 - Rotifers).

Generally speaking, the colonization of a raw effluent can be divided in three stages (Madoni, 1994; Nicolau et al., 1997):

First stage → Characterized by the presence of the ‘pioneer’ species such as the flagellates and the free-swimming ciliates which are independent of the incoming raw effluent. These species are not regarded as the typical constituents of an activated sludge process.

Second stage → With the beginning of the sludge formation the flagellates and free-swimming ciliates disappear progressively whereas sessile and crawling ciliates increase both in number as in species.

Third stage → Population structure reflects the established conditions as a function of the balance between the organic load and the produced, removed and recycled sludge.

Microscopic observations of protozoa and metazoa are rather common and may provide precious indications of the performance of a plant, the final effluent quality and the presence of toxic substances. The following characteristics must be present in a satisfactory working plant (Madoni, 1994a):

High protozoa density (>103 per ml). Dominant sessile and crawling ciliates. Well diversified population with no overwhelming specie(s).

When such is not the case, the dominant specie(s) identification may allow for the plant diagnostic as shown in Table 1.6 (Nicolau et al., 1997).

High Load Medium/Low Load Aerated Sludge Age

1

2 3 4 5

Num

ber



Table 1.6 – Relationships between dominant groups, efficiency and problem causes (adapted from Nicolau et al., 1997).

Dominant group Efficiency Probable problems cause

Small flagellates Poor Sub-aeration, incoming fermentescible substances

Small naked amoeba Flagellates Poor High load and/or non-degradable

Small free-swimming ciliates Mediocre Low HRT, sub-aeration Large free-swimming ciliates Mediocre High load

Sessile ciliates Small Transient phenomena Crawling and sessile ciliates Good

Crawling ciliates Good Testate amoeba Good Low load and/or diluted, nitrification

As already mentioned, the optimal plant efficiency occurs when a correct balance between crawling and sessile ciliates and metazoa is achieved. An overpopulation of flagellates, sarcodines or free-swimming ciliates reflects high organic loads (high F:M ratio), whereas the dominance of sessile ciliates and metazoa reveals the opposite but, in both cases the effluent settleability is low. A good effluent quality is achieved only when a well stabilized diversified community is attained (Richard, 1989).

Protozoa and metazoa are also reliable indicators of the presence of toxic substances, mainly ciliates and rotifers which are quite sensitive to the presence of toxics and other stress conditions, acting like biological pollutant indicators. These micro-organisms are particularly susceptible to heavy metals, cyanide, low oxygen or temperatures above 40 ºC. The stoppage or slowing down of the cilia in the ciliates is generally the first sign of these phenomena, followed by a shift in the dominant group in the mixed liquor to flagellates and small free-swimming ciliates, which may also suppose a break-down of the flocs and consequent higher turbidity due to the release of bacteria to the medium. In extreme cases all protozoa species may even completely disappear (Richard, 1989). The usefulness of toxicological essays with protozoa, mainly through the study of their feeding behaviour and organic matter removal efficiency in activated sludge processes, has been further demonstrated by Nicolau et al. (2001).

1.3.2.2 PROTOZOA AND METAZOA SYSTEMATIC

Protozoa are a well diversified group of mainly unicellular organisms classified in the Protista kingdom. They are mainly bacterivore ingesting bacteria, but also algae and other protozoa. Generally, each individual is unicellular although, their size, shape and organization are extremely variable and quite adaptable so, they have colonized every aquatic environment and evolved to several different groups. Unicellular as they are, single cells had to adapt in order to accommodate every functions of a multicellular individual. Although the exact number of different protozoa species is yet not known, at least 50.000 species have been accounted for (Jahn et al., 1999; Fenchel, 1999).

Protozoa classification is ordinarily accomplished by large categories containing similar groups, mainly on their locomotion skills. The three major categories of protozoa are (Richard, 1989; Madoni, 1994; Canler et al., 1999):



Flagellates → Move by flagella. Common on the first stages of the raw effluent colonization their presence indicates fresh sludge, sudden organic load increase or lack of oxygen. They are resistant to anoxic conditions and toxics, indicative of poor final effluent quality.

Sarcodines → Move by pseudopodia. Small naked amoebas correlate with small flagellates being associated to transient phenomena and a mediocre final effluent. Large naked amoebas are also connected to transient phenomena but indicate a good final effluent. Testate amoebas are representative of low organic loads, aged sludge, good final effluent quality and nitrification.

Ciliates → Move by small locomotion structures dispersed throughout their body called cilia. They represent around 70% of the protozoa in a well established wastewater treatment plant and are quite sensitive to plant conditions. Ciliates take an important role in water purification which implies a strict relation between their number and final effluent quality, prevailing in medium or low loads.

Ciliates can be divided into 4 major groups. The balance between all four groups in a wastewater treatment plant is established by the organic load. When in low or medium load there is a good equilibrium between the Holotrichia, Peritrichia and Spirotrichia sub-classes (Canler et al., 1999).

Holotrichia → When dominant indicate transient phenomena. Small size species are related to low Hydraulic Retention Times or sub-aeration whereas large size species refer to organic overloads.

Peritrichia → Present in all organic loads but become dominant in low loads or sludge wash out.

Spirotrichia → Found in low loads or aged sludge, they increase in well established plants and are related to good treatment efficiencies.

Suctoria → Not very common, they are present mostly in good quality final effluents.

Most of ciliates nourish on bacteria but a few graze on other ciliates or flagellates. According to Madoni (Madoni, 1994) bacterivore ciliates may be divided in 3 groups:

Free-swimming → Moving freely in the effluent and remaining suspended in the aeration tank, they indicate transient phenomena and a mediocre final effluent. Small size species reveal low HRT or sub-aeration whereas large size species refer to organic overloads.

Crawling → Living and grazing in the floc surface they indicate a medium to good final effluent quality. Their number increase in well established plants and medium to low organic loads.

Sessile → Attached to the flocs by a stalk they are present in all loads but dominate in low loads or sludge wash out.

A population able to attach itself to the flocs, such as the sessile, or remain connected, as the crawling, possesses clear advantages over a freely dispersed one, which can be easily dragged out in the final effluent instead of settling with the flocs in the



settling tank. Also, most of the ciliates nourish on bacteria implying a direct competition for the freely dispersed bacteria by the sessile and free-swimming unlike the crawling species that graze by scratching the flocs surface.

Metazoa are multicellular animals where each cell is unable to perform all the bodies’ functions unlike protozoa. They feed on bacteria and have longer gestation times which imply more aged sludge than protozoa. They contribute to the flocculation process in a twofold manner: by means of mucus secretion in which new filamentous and floc-forming bacteria can adhere and by fragmentizing hardly settleable large flocs in their motion. They are considered as reasonable indicators of the wastewater treatment efficiency (Jahn et al., 1999). The most common metazoa present in activated sludge wastewater treatments are (Richard, 1989; Madoni, 1994):

Rotifers → The most representative of the metazoa found in wastewater treatment plants they indicate an aged sludge with good aeration and final effluent quality.

Nematodes (Gastotrichia) → Present in small numbers in all loads they are resistant to sub-aeration periods but not correlated with a specific effluent quality.

Annelids (Oligotrichia) → Present only in aged and well established sludge in very low loads they reveal a good final effluent quality and nitrates.

Although more than 230 different species of protozoa were identified in wastewater treatment plants only a few of these are found frequently. Curds et al. (1970a) were one of the first groups to study and systematize the most common micro-organisms found in any plant, which was latter prolonged by Madoni (1994a) as shown in Table 1.7.

Table 1.7 – Most common ciliate protozoa in wastewater treatment (adapted from Madoni, 1994a). Bacterivore Carnivorous

Free-swimming Crawling Sessile Acineria incurvata Acineria uncinata Aspidisca cicada Carchesium spp. Acineta spp. Colpidium campylum Aspidisca lynceus Epistylis spp. Amphileptus sp. Colpidium colpoda Chilodonella uncinata Opercularia coarctata Coleps hirtus Colpoda sp. Euplotes affinis Opercularia microdiscus Litonotus spp. Cyclidium glaucoma Euplotes moebiusi Opercularia minima Matacineta spp. Drepanomonas revoluta Euplotes patella Stentor spp. Podophrya spp. Glaucoma scintilans Stylonychia spp. Vaginicola crystallina Spathidium spp. Paramecium spp. Trithigmostoma cucullus Vorticella aquadulcis Tokophrya spp. Pseudocohnilembus pusillus Trochilia minuta Vorticella convallaria Spirostomum teres Vorticella microstoma Tetrahymena pyriformis Zoothamnium spp. Thrachelophyllum pusillum Uronema nigricans


Throughout the years several authors have attempted to monitor wastewater plant conditions by the screening of protozoa population. Curds et al. (1970) was one of the first



teams to build a classification table based on protozoa observation, where they associated the effluent quality, in terms of BOD5, to the protozoa population. Madoni pursued the same objective with the elaboration of a Sludge Biotic Index (SBI), which correlated the number of found species from each taxonomic group, as well as some species in specific, with the effluent quality in terms of organic load, aeration and treatment efficiency (Madoni, 1994a). In this work the most common protozoa and metazoa found in well established wastewater treatment plants were studied. Consequently a few species of flagellate, sarcodine and ciliate protozoa, as well as rotifers, gastotrichia and oligotrichia were surveyed. Ciliate protozoa correspond to about 70% of total protozoa in a plant and concentrations of 104 per ml, which may reach 250 mg/L or 10% of the volatile total solids (Madoni, 1994b, Madoni, 1994c) and, for that reason, these particular protozoa were studied in more detail.

The protozoa and metazoa used in this work were the most commonly found in the mixed liquor of wastewater treatment plants and are described in Table 1.8.

Table 1.8 – Studied protozoa. Flagellate Peranema

Arcella sp. Sarcodine Euglypha sp.

Free-swimming Trachelophyllum sp. Litonotus sp. Carnivorous Suctoria (Sub-class) Trithigmostoma sp. Trochilia sp. Aspidisca cicada

Crawling

Euplotes sp. Opercularia sp. Epistylis sp. Vorticella (Genus) V. microstoma V. aquadulcis Zoothamnium sp.

Protozoa

Ciliate

Sessile

Carchesium sp. Digononta (Order) Rotifer Monogononta (Order)

Gastotrichia Nematoda (Sub-Class)

Metazoa

Oligotrichia Aelosoma sp.

The most common operating conditions susceptible of monitoring by the protozoa and metazoa survey are the organic load applied to the aeration tank, the oxygen availability (aeration), the sludge age, the presence of nitrification and finally the final effluent quality. Furthermore, and in order to allow the assessment of the wastewater treatment plant operating conditions, the representative micro-organisms of each condition must be specified. Therefore, and accordingly to Madoni (1994a), Jahn et al. (1999) and Canler et al. (1999), the main relationships found between the micro-organisms and the plant operating conditions are summarized in Table 1.9.



Table 1.9 – Relationships between protozoa and metazoa and plant operation conditions (NI represents no indication).

Organic Load

Nitrification Sludge Age

Aeration Effluent Quality

Aspidisca cicada NI NI Medium NI NI Aelosoma sp. Low Presence Old Good Good

Arcella sp. Low Presence Old Good Good Carchesium sp. Low Presence Medium Good Good

Digononta Low NI Old NI NI Epistylis sp. Low Presence Medium Medium Good Euglypha sp. Low NI Old Good Good Euplotes sp. Low Presence Medium Medium Good

Litonotus sp. NI NI Medium NI NI Monogononta Low Presence Old Good Good

Nematoda NI NI NI Poor NI Opercularia sp. High NI NI Poor Mediocre Peranema sp. Low NI Fresh Medium Good

Suctoria NI NI Medium NI NI Trachellophyllum sp. High NI NI Medium Mediocre Trithigmostoma sp. NI NI NI Medium Good

Trochilia sp. Low Presence Medium Good Good V. aquadulcis Low NI Medium Good Good V. microstoma High NI Fresh Poor Mediocre

Vorticella NI NI Medium NI NI Zoothamnium sp. Low NI Medium Good Good

Two of the major drawbacks in using protozoa and metazoa in wastewater treatment plants diagnosis reside on the need of zoology or even protozoology skilled technicians. Hence, image processing and analysis can be seen as a potentially very important tool in order to overcome this difficulty. However, and up to the moment, image analysis has not been widely used in characterizing and/or classifying protozoa and metazoa in general and even less in wastewater treatment plants. As a matter of fact few have been the studies about morphological characterization of protozoa and metazoa. The works of Amaral et al. (1998; 1999; 1999a; 2001), Baptiste (1998), da Motta et al. (da Motta et al., 1999; 2000; 2001; 2001a; 2001b; 2001c; 2001d) and Golz et al. (Golz et al., 2001) are among the few studies published on this subject.

Therefore, the main objective of this work resided on the development of an image processing and analysis programme to morphologically characterize the studied protozoa and metazoa, which is further detailed in Section 2.3.1. In order to recognize and classify the protozoa and metazoa the morphological parameters collected were used as raw data for multivariable statistical techniques such as Discriminant Analysis (Einax et al., 1997) and Neural Networks (Kasabov, 1996; Leondes, 1998). In brief, Discriminant Analysis allows for the determination of new variables (discriminant functions) as linear combinations of the raw parameters whereas Neural Networks determine linear and non-linear functions connecting the inputs to the outputs. Both techniques allow the recognition and classification of each protozoan and metazoan from its morphological parameters and, are further detailed in Section 2.5.



General Introduction Anaerobic Wastewater Treatment Processes 1.4


1.4 ANAEROBIC WASTEWATER TREATMENT PROCESSES

In this section the anaerobic wastewater treatment process is discussed taking into consideration its basis and technological issues with a special focus on the granulation process and the granule deterioration problematic. The main steps of the anaerobic treatment are introduced and the main reactor operating parameters are discussed.

The granulation process is discussed in terms of the microbial aggregation process, aggregates morphology and its dependence on the operating parameters.

With respect to the granule deterioration its effect on the biomass is analysed as well as the corresponding changes on the aggregates morphology.

Finally, the objectives of the granulation process and the granule deterioration triggered by oleic acid studies by image analysis methods are explained.

General Introduction 1.4 Anaerobic Wastewater Treatment Processes


1.4.1.1 ANAEROBIC TREATMENT BASIS AND TECHNOLOGY

Anaerobic digestion can be seen as a series of microbiological processes commonly found in several natural environmental such as sediments, termites, rumen and oil fields that degrade organic compounds to methane. The microbiological nature of methanogenesis was determined more than one century ago (Koster, 1988) and, whereas in activated sludge processes a wide diversity of organisms is involved, anaerobic processes are driven mostly by bacteria. In fact, a large number of strict and facultative anaerobic bacteria are involved in the hydrolysis and fermentation of organic compounds. Synergistic interactions between this mainly bacterial consortium of micro-organisms, are responsible for the transformation of complex organic compounds with high molecular weights to methane, carbon dioxide, molecular hydrogen, ammonia and hydrogen sulphide (H2S) (Polprasert, 1989). In a simplified manner, the anaerobic digestion can be represented as four major degradation sequences, as shown in Figure 1.5: hydrolysis, acidogenesis, acetogenesis and methanogenesis. For each of these four major degradation sequences there are different types of bacteria involved (Bitton, 1994):

Hydrolytic Bacteria (Hydrolysis) → These bacteria, which include Bacteroides, Bifidobacterium, Clostridium and Lactobacillus, hydrolyze complex organic molecules (proteins, cellulose, lignin, lipids) into soluble monomer molecules (amino acids, glucose, fatty acids and glycerol) directly available to the fermentative acidogenic bacteria. The hydrolytic step is achieved by extra cellular enzymes such as cellulases, proteases, and lipases, being relatively slow and thus the limiting step of the anaerobic digestion of complex substrates (Polprasert, 1989; Speece, 1983).

Fermentative Acidogenic Bacteria (Acidogenesis) → Acidogenic, or acid-forming, bacteria convert sugars, amino acids and fatty acids to organic acids such as acetic, propionic, formic, lactic, butyric and succinic acid, alcohols and ketones (ethanol, methanol, glycerol and acetone), acetate, carbon dioxide and molecular hydrogen. The products formed vary with the type of bacteria, culture conditions (pH, temperature) and carbon source. The number and composition of these bacterial consortia is dependent on the raw substrate, but represents about 90% of the total bacterial population in an anaerobic digester (Zeikus, 1980). These bacteria have a high growth rate and, therefore, the acidogenesis never is the limiting step on the anaerobic digestion process (Gujer and Zehnder, 1983).

Acetogenic Bacteria (Acetogenesis) → These bacteria, also called syntrophic bacteria or obligate hydrogen producing acetogens (OHPA) convert organic acids and alcohols into acetate, molecular hydrogen and carbon dioxide, which are latter used by the methanogenic bacteria. There is a strong symbiotic relationship between acetogenic bacteria such as Syntrophomonas and Syntrophobacter and methanogens, because this latter group helps to achieve the low hydrogen conditions required for acetogenic conversions to proceed. It is thought that, in order to this interaction to take place, a close proximity between these two groups is required (Gujer and Zehnder, 1983), which may justify to some extent the granulation phenomena (Schink and Thauer, 1987).

Methanogens (Methanogenesis) → Methanogenesis is the final step of the anaerobic digestion and, in most cases, the limiting step of the process. Methanogenic bacteria occur naturally in deep sediments or in the rumen of herbivores and grow slowly in the wastewater with generation times ranging



from 3 days at 35 ºC up to 50 days at 10 ºC. Methanogens degrade only a small number of substrates such as acetate, methanol, methylamines, formate and carbon dioxide, and can be divided in two sub-categories:

Hydrogenotrophic methanogens → Hydrogen using chemolithotrophs such as Methanosarcina, Methanobrevibacter and Methanococcus convert hydrogen and carbon dioxide to methane. These methanogens help to maintain the low hydrogen levels needed for the conversion of the volatile acids and alcohols to acetate (Speece, 1983).

Acetotrophic methanogens → Also called acetoclastic or acetate-splitting bacteria, convert acetate into methane and carbon dioxide, and are responsible for about two-thirds of the formed methane (Mackie and Bryant, 1981). These bacteria have a high susceptibility to adverse conditions such as organic and hydraulic shock loads, and toxicants presence, with a growth rate much smaller (generation time of a few days) than the acid-forming bacteria and comprise two main genera: the metabolically versatile Methanosarcina (Smith and Mah, 1978) and the strict acetotrophic Methanosaeta (Huser et al., 1982).

The main bacterial groups involved in anaerobic digestion and their relationships are expressed in Figure 1.5.

Figure 1.5 – Bacterial groups involved in anaerobic digestion (adapted from Gujer and Zehnder,

1983).

Complex Organic Molecules

Amino Acids, Sugars Fatty acids

Intermediate Products: Propionate, Butirate, Ethanol

Hydrogen

Hydrolysis

Acidogenesis

Acetogenesis

Methanogenesis

Carbon Hydrates Proteins Lipids

Acetate

Methane



Anaerobic digestion is affected by the environmental and operating conditions, such as pH, temperature, Hydraulic Retention Time (HRT) and nutrients among others. The temperature within the digester must be within the range of 25 ºC to 40 ºC for mesophile strains, and between 30 ºC to 35 ºC for an optimum growth of the methanogens. The Hydraulic Retention Time must be long enough to allow the anaerobic bacteria metabolism to degrade the complex organic compounds. The digesters based on attached biomass have a lower HRT, ranging from 1 to 10 days, than the ones based on dispersed growth, between 10 to 60 days (Polprasert, 1989). Most of the methanogenic bacteria operate between pH values of 6.7 to 7.4 with an optimum value around pH 7. Acidity and pH levels depend on a well balanced production and removal of organic acids by the different consortia of the bacteria in the digester. Therefore, upsets on the environmental conditions of the digester can alter the balance between alkalinity and acidity levels, and correcting measures must be taken. Wastewaters must be nutrionally balanced in terms of carbon (C), nitrogen (N), phosphorous (P) and sulphur (S) to maintain an adequate anaerobic digestion. According to Sahm (1984) the C:N:P ratio must be of 700:5:1. Methanogens and sulphate-reducing bacteria compete for the same electron donors, acetate and molecular hydrogen. The acetate level must be kept fairly high in order to prevent sulphate-reducing bacteria outnumbering methanogen bacteria and therefore, allow the methanogenesis to take place, instead of sulphate reduction (Lawrence et al., 1966; McFarland and Jewell, 1990). Finally, toxicants (from an anaerobic consortia viewpoint) such as oxygen, ammonia, chlorinated hydrocarbons, benzene ring compounds, heavy metals and long-chain fatty acids among several others, may also result in occasional failures of anaerobic digesters.

There are two major structures by which wastewater treatment microbial agglomerates can be found: self-aggregated biomass (most common) and attached biofilms, although in some particular cases a combination of the above mentioned structures can be seen. The main types of wastewater treatment anaerobic systems supporting these types of biomass are (Bitton, 1994): septic tanks; up-flow anaerobic sludge blankets; anaerobic attached-film, expanded-bed and fluidized-bed reactors; and anaerobic rotating biological contactors. From these the most commonly used reactors are the up-flow anaerobic sludge blankets with a worldwide implementation of around 70%.

The up-flow anaerobic sludge blankets consist of a bottom layer of packed sludge (sludge blanket) and an upper liquid layer, as shown in Figure 1.6 (adapted from Lettinga et al., 1980). Wastewater flows upwards through a sludge bed covered by a bacterial aggregates floating blanket with settler screens separating the sludge aggregates from the treated wastewater and a gas collector on top of the reactor (Schink, 1988). The resulting granular sludge, usually between 1 to 5 mm in diameter, has high VSS contents and specific activity and good settling properties. As previously mentioned, up-flow type reactors are among the most widely used systems in anaerobic wastewater treatment either as up-flow anaerobic sludge blankets (UASB) or expanded granular sludge blanket (EGSB) reactors obtained by means of effluent recirculation. Both of these reactors are able to support a solid retention time diverse from the Hydraulic Retention Time and require no support material.



Figure 1.6 – Up-flow anaerobic sludge blanket (UASB) reactor (adapted from Lettinga et al., 1980).

1.4.1.2 THE GRANULATION PROCESS

In the late seventy’s granular aggregates were for the first time described, leading to great developments on the anaerobic treatment processes, such as the design of new reactors where the immobilization of the biomass was based solely on the aggregates density and size (Lettinga et al., 1980). In these types of reactors (UASB and EGSB) two different zones are present: a lower layer with settled granulated biomass highly concentrated and a top layer with a smaller amount of biomass as small and hardly settleable flocs. The granules can be considered as spherical-like biofilms, formed by auto-immobilization with an internal microbial organization responsible for their particular properties. For instance, the proximity between the syntrophic bacterial groups (acetogens and methanogens) favours metabolite exchanges and the location of the sensible acetoclastic methanogens in the inner core of the granule protects them from environmental upsets (Alves et al., 1999). Accordingly to Schmidt and Ahring (1996), in the inner most layers of the granules are also found the hydrogen producing acetogenic bacteria whereas the fermentative acidogenic bacteria and the hydrogenotrophic methanogens occur in the outer most layers.

The development of predominantly Methanosaeta filamentous biomass is favoured in most anaerobic digesters by the prevailing conditions of low effluent acetate concentrations and low selection pressures (Wiegant, 1987). By the contrary in high selection pressures only the aggregated biomass will remain inside the reactor leading to the predominance of Methanosarcina and Methanosaeta aggregates. The selection pressure is originated from both the hydraulic and the gas loading rate and acts on the basis of the density differences between free organisms and bacterial agglomerates, leading to a wash-out of the dispersed organisms (Hulshoff Pol et al., 1987).

The maintenance of the granular structure inside a digester is one of the most important factors in anaerobic digestion. Under adverse conditions granular biomass may deteriorate affecting the reactor stability and leading to biomass wash-out and ultimately to a loss of the reactor performance. The granulation process is, by definition, the process by which the biomass aggregates to form well defined individual granules, quite complex and involving diverse bacterial trophic groups and chemical, physical and microbial

Raw Effluent

Gas Outlet

Treated Effluent

Sludge

Liquid

Settler / Gas separator



interactions. Although the phenomenon of granulation has been studied from several different viewpoints (Hulshoff Pol, 1989; Schmidt and Ahring, 1996), and a large set of chemical and physical data is now available, a unique granulation theory has yet failed to be widely recognized. For instance, Fang (2000) reports the granular structure dependence on the substrates nature and Batstone (2001) supports the prevailing role of the operating parameters such as the up-flow velocity and reactor design.

However a few theories about the formation of stable and settleable granules have been published and, according to some authors (Wu et al., 1996; Hulshoff Pol et al., 1983; Wiegant, 1987), the granulation process starts with the formation of a Methanosarcina and Methanosaeta nuclei. Methanosarcina growths in aggregates due to exopolymers excretion and the further increase in size of these agglomerates will prevent their wash-out from the reactor. The ability of the rod-shaped Methanosaeta to attach to inert surfaces is responsible to the bacteria adsorption to the inner cavities of the Methanosarcina aggregates in increasing acetate concentrations (de Zeeuw, 1987) making the aggregates larger and more robust. The renewal of the cells within the aggregates (maturation process) is responsible for the disappearance of the filamentous aggregates replaced by new ones with increased granulation properties. Furthermore, during the growing process of the aggregates, small portions tend to liberate and act as nuclei for the formation of second and third generation aggregates. Hulshoff Pol et al. (1987) alert to the density differences between the dispersed fraction of the biomass, easily washed out from the reactor, and the aggregated biomass resting inside the reactor. The selection pressure resulting from the combination between the hydraulic load and the gas flow rate seems to be responsible for the non-aggregated biomass wash-out.

During the granulation process, three types of agglomerates, as shown in Figure 1.7, can be distinguished accordingly to Dolfing (1987) and Dubourguier et al. (1988):

Flocs → Low-structured agglomerates which, after settling form a macroscopically single layer. These aggregates appear to have a strong filamentous constitution mainly of Methanosaeta bacteria.

Pellets → Denser than the flocs which, after settling remain separate entities. These agglomerates seem to be composed by clumps of Methanosarcina but mainly by filamentous Methanosaeta strongly attached in spaghetti-like rounded structures up to 1 cm in diameter.

Granules → Granular-like denser pellets, with some pressure resistance, thus non-deformable in the presence of water. These aggregates are mainly composed by both clumps of Methanosarcina and filamentous Methanosaeta strongly attached and form rounded aggregates up to 5 mm in diameter.

Figure 1.7 – Anaerobic biomass agglomerates: a) flocs. b) pellets. c) granules.

(a) (b) (c)



1.4.1.3 GRANULE DETERIORATION

Wastewater effluents containing lipids are rather problematic to the anaerobic granular biomass, as their hydrolysis result in the formation of long chain fatty acids (LCFA). Among these, oleic acid is an abundant LCFA in wastewaters and may cause problems to the anaerobic biomass because it is toxic to acetogenic and methanogenic bacteria and adsorbs onto the sludge, inducing sludge flotation and wash-out (Rinzema, 1988; Hwu, 1997). The granular biomass will be likely to disintegrate and become encapsulated by a whitish, light and gelatinous mass. Moreover, LCFA at neutral pH act as surfactants lowering the surface tension and therefore, hindering the aggregation of hydrophobic bacteria, such as most acetogen LCFA-degraders and promoting their wash-out (Daffonchio et al., 1995). Accordingly to Hwu (1997), the LCFA concentration capable of provoking flotation problems is far below the toxicity limit, suggesting that the wash-out of granular sludge occurs prior to inhibition. Furthermore, the addition of calcium salts prevents inhibition to some extent, but does not prevent flotation (Hanaki et al., 1981).

The operation of granule-based anaerobic digesters, such as the UASB and EGSB is thus, largely affected by wastewater effluents containing problematic substrates such as lipids. Accordingly to Hwu et al. (1997a), typical EGSB operating conditions (HRT bellow 10 hours with an Up-Flow Velocity higher than 4 m/h) can not be applied when treating an oleic acid-based effluent. Furthermore, they determined an optimum methane conversion at Hydraulic Retention Times around 24 hours as well as the use of washed out biomass recycling that exhibited higher oleic acid degradation capabilities than the biomass remaining inside the reactor.

It is accepted that filamentous organisms play a key role in the process of granulation, being responsible for the first nuclei of aggregated biomass. In the granule deterioration process the filaments behaviour is not well known, but are possibly released to the bulk medium.


The processes of granulation and granule-deterioration are usually coupled with a macroscopic transformation of size and morphology. These changes can be quantified by image analysis, although most of the published works so far are mainly focused on size determinations (Dudley et al. 1993, Jeison and Chamy, 1998). Among the most relevant studies up to date are Bellouti et al. (1997), that used image analysis to differentiate anaerobic flocs and granules by the measurement of fractal dimensions, Howgrave-Graham and Wallis (1993), that quantified the bacterial morphotypes within anaerobic granules from transmission electron micrographs by image analysis, Amaral et al. (1997) and Hermanowicz et al. (1995) on the fractal dimension of microbial aggregates.

Systematic microscopic examinations have not been widely used so far to follow the granulation and deterioration processes, because they are tedious and difficult to implement in a quantitative way. However, the use of image analysis coupled to microscopic observations helps overcoming this problem and allows the study of the physical properties of the filamentous biomass and microbial aggregates. Among these, the most importants seem to be the filaments content, and the aggregates size distribution, settling velocity and morphology, which can be used to monitor the stability of the digester allowing the observation of shifts between floccular and granular biomass (Bellouti et al., 1997). The use of image analysis techniques may, therefore, provide for a



continuous optimization of the granular process by monitoring the dynamic changes along the process and the identification of critical moments, like the granulation time (Singh et al., 1998). Bearing this purpose in mind, image analysis tools were used to obtain valuable information about the formation, deterioration and morphological changes upon the growth of the bacterial aggregates into granules.

The aims of the present study can be divided into two major axes as follows: Identification of the critical moments on the anaerobic granulation process, by

surveying the aggregates morphological parameters and filaments contents. Monitor granular sludge deterioration from an EGSB reactor fed with

increasing loads of oleic acid and quantify the morphological changes in the granular sludge by surveying the aggregates morphological parameters and filaments contents.

69

2 MATERIALS AND METHODS

70

2.1 Experimental Surveys 71 2.1.1 Activated sludge monitoring 72 2.1.1.1 Activated Sludge Experimental Survey 72 2.1.1.2 Activated Sludge Image Acquisition and Processing 73 2.1.2 Protozoa and metazoa identification 74 2.1.2.1 Protozoa and Metazoa Experimental Survey 74 2.1.2.2 Protozoa and Metazoa Image Acquisition and Processing 75 2.1.3 Anaerobic granulation process monitoring 76 2.1.3.1 Anaerobic Granulation Experimental Survey 76 2.1.3.2 Anaerobic Granulation Image Acquisition and Processing 77 2.1.4 Granule deterioration triggered by oleic acid 79 2.1.4.1 Granule Deterioration Experimental Survey 79 2.1.4.2 Granule Deterioration Image Acquisition and Processing 80 2.2 Operating parameters 81 2.2.1 Activated sludge operating parameters 82 2.2.2 Anaerobic digestion operating parameters 83 2.3 Image Processing 85 2.3.1 Protozoa and metazoa image processing 86 2.3.2 Flocs image processing 96 2.3.3 Granules image processing 104 2.3.4 Filaments image processing 112 2.4 Morphological Parameters 123 2.4.1 Euclidean morphological parameters 124 2.4.2 Fractal dimensions 132 2.5 Multivariable Statistical Techniques 135 2.5.1 Partial least squares 136 2.5.2 Discriminant analysis 138 2.5.3 Neural networks 140

Materials and Methods Experimental Surveys 2.1


2.1 EXPERIMENTAL SURVEYS

In this section the experimental set-up, operating parameters and image acquisition methodologies for the activated sludge monitoring∗, protozoa and metazoa identification∗∗, granulation process∗∗∗ and granule deterioration∗∗∗∗ are explained.

For the activated sludge monitoring, the surveyed wastewater treatment plant operating parameters are described and the studied operating and morphological parameters are presented. The aggregates and filaments image acquisition methodology is also discussed.

For the protozoa and metazoa identification, the surveyed wastewater treatment plants are described and the studied morphological parameters are presented. The protozoa and metazoa image acquisition methodology is also discussed.

For both the granulation and the granule deterioration processes, the surveyed digester operating parameters are described and the studied operating and morphological parameters are presented. The aggregates and filaments image acquisition methodology is also discussed.

∗ In the activated sudge monitoring work the activated sludge sample collection was

performed by Sofia Rodrigues as part of her final stage work and is fully detailed in Rodrigues (2000).

∗∗ In the protozoa and metazoa identification work the protozoa and metazoa sample collection from the French wastewater treatment plants was performed by Maurício da Motta as part of his PhD thesis and is fully detailed in da Motta (2001).

∗∗∗ The operation of this reactor is part of the PhD thesis of Pablo Araya-Kroff and is fully detailed in Araya-Kroff et al. (2002). The sample collection is part of the PhD thesis of Pablo Araya-Kroff and of the MSc thesis of Lúcia Neves and is fully detailed in Neves (2002).

∗∗∗∗ The operation of this reactor is part of the PhD thesis of Alcina Pereira and is fully detailed in Pereira et al. (2001).

Materials and Methods 2.1 Experimental Surveys


2.1.1 ACTIVATED SLUDGE MONITORING 2.1.1.1 ACTIVATED SLUDGE EXPERIMENTAL SURVEY

In this work the biomass (activated sludge) present in the aerated tanks of the domestic sewage treatment plant of AGERE, E.M. in Frossos (Braga) was followed for a period of three and a half months (from 03 of March to 15 of June of 2000). In this wastewater treatment plant, as shown in Figure 2.1, the wastewater is first subjected to a preliminary treatment (screening, flotation), then passes by the primary settlers, followed by the aeration tanks, the secondary settlers for sludge settling purposes, where the sludge is recycled back to the aeration tanks and the final treated effluent is released to the near by river and, in lesser amount, used to the wastewater plant cleaning. The excess of sludge from the secondary settlers is treated in the anaerobic digesters and then dehydrated. The methane obtained by the anaerobic process is stored in the gas tanks and used to heat up the biomass prior to the anaerobic digestion and for energy generation. This wastewater treatment plant receives a flow rate around 23000 m3/day of a predominantly urban sewage (90%) with COD values about 450 mg/L, where roughly 45% corresponds to BOD5. Around 15000 m3 per day are recycled back from the secondary settlers to the aerated tanks corresponding to a Hydraulic Retention Time between 7 to 8 hours, and around 800 m3 of sludge are treated each day by the anaerobic digesters.

Figure 2.1 – Braga wastewater treatment plant.

The purpose of this study focused on the enlightenment of the relationships between the filamentous contents and sludge morphological properties with plant operation conditions and the chemical and physical activated sludge properties. All the following operating parameters were provided by Frossos wastewater treatment plant laboratory and are fully detailed in Rodrigues (2000): Total Suspended Solids, Sludge Volume Index, and Hydraulic Retention Time were determined by the methods described in Section 2.2.1. Two image analysis programs were then created in Matlab (The Mathworks, Inc., Natick) to provide the following sludge data: Aggregate Area, Total Aggregate Area, Equivalent Diameter, Eccentricity, Convexity, Compactness, Solidity, Roundness and Extent in terms of flocs morphology and Filament Length, Total Filament Length, Total Filaments Length per Total Aggregates Area Ratio and Total Filaments Length per Total Suspended Solids in terms of filamentous bacteria contents (all of these parameters are further described in

Primary Settlers

Aeration Tanks Secondary

Settlers

Gas Tanks Digesters

Screening Flotator



Section 2.4). Finally, the multivariate statistical technique Partial Least Squares (PLS) was used in order to relate the image processing data with the chemical and physical activated sludge properties.

2.1.1.2 ACTIVATED SLUDGE IMAGE ACQUISITION AND PROCESSING

Filament image acquisition was accomplished through phase contrast microscopy on a Zeiss Axioscop microscope (Zeiss, Oberkochen) with a 100 times magnification. Aggregates larger than 0.0182 mm in equivalent diameter were acquired through the visualization on a SZ 4045TR-CTV Olympus stereo microscope (Olympus, Tokyo) with a total magnification of 40 times (maximum magnification). In all the cases the microscopes were coupled to a Sony CCD AVC–D5CE (Sony, Tokyo) grey scale video camera. Computer image grabbing was performed in 8 bit (256 grey levels) 768x576 pixels matrix by a Data Translation DT 3155 (Data Translation, Marlboro) frame grabber using the commercial software package Image Pro Plus (Media Cybernetics, Silver Spring).

The activated sludge sample collection was performed by Sofia Rodrigues as part of her final stage work of Biological Engineering graduation and is fully detailed in Rodrigues (2000). All the samples were collected from the aerated tanks of Braga wastewater treatment plant and, in all cases, the maximum period of time between sample collection and image acquisition did not exceed 3 hours with no more than half an hour without aeration. For the filaments and aggregates image acquisition, a volume of 50 µL was taken to a slide and covered with a 24x24 mm cover slip for visualization and image acquisition in phase contrast and direct light respectively. For the aggregates image acquisition around 25 images were acquired for sample and for the filaments around 30 images were acquired for sample.

Image acquisition of the flocs and filaments on the slide was obtained by three passages at one quarter, half and three quarters of the slide, as shown in Figure 2.2, and in all horizontal passages an image is acquired at each 4 step-lengths in a total of 18 images for each slide. This methodology intends to minimize non-uniform flocs deposition in the slide. Optimal focusing was then sought in order to enhance the flocs borders. The metric unit dimensions for each magnification were further calibrated to pixels with the help of a micrometer slide.

Figure 2.2 – Image acquisition methodology within each slide.

The image analysis and processing for the flocs and filaments was achieved by means of specially developed programmes in Matlab (The Mathworks, Inc., Natick) called Flocs and Filaments respectively. Both of these programmes are detailed further on in Section 2.3.

1/4

3/4

1/2



2.1.2 PROTOZOA AND METAZOA IDENTIFICATION 2.1.2.1 PROTOZOA AND METAZOA EXPERIMENTAL SURVEY

The studied protozoa and metazoa were collected from the aerated tanks of wastewater treatment plants from Portugal and France as follows: Nancy (Maxéville) with domestic and brewery sewages and Braga with domestic sewages. A few protozoa and metazoa were also gathered from the wastewater treatment plants treating domestic sewage of Dameleviéres, Dombasle, Liverdun, Pont-à-Mousson, Toul, Metz, Aix-en-Vienne, Isle-sur-Vienne, Caminha and Gelfa.

Nancy wastewater treatment plant, shown in Figure 2.3, treats a domestic and brewery sewage with an incoming flow rate of 100000 m3/day, a COD of 260 mg/L and a BOD5 of 120 mg/L.

Figure 2.3 – Nancy wastewater treatment plant.

As previously referred in Section 2.1.1 Braga wastewater treatment plant treats a domestic sewage with an incoming flow rate of 23000 m3/day, a COD of 450 mg/L and a BOD5 of 245 mg/L.

The purpose of this study focused on protozoa and metazoa identification and classification present in any given wastewater treatment plant by means of a semi-automatic image processing procedure. With this aim, an image analysis programme was created in Visilog (Noesis, S.A., les Ulis) to provide the following morphological parameters: Area, Equivalent Diameter, Perimeter, Length, Width, Feret Shape, Eccentricity, Shape Factor, Robustness, Concavity Index, Concavity Ratio, Convexity, Compactness, Solidity, Average Width, Perimeter Factor, Stalk Length, Stalk Average Width, WSWBA Ratio, WBAWB Ratio and Euclidean Distance Map Fractal Dimension (these parameters are further described in Section 2.4). Finally, Discriminant Analysis (DA) multivariate statistical technique and Neural Networks (NN) were used to allow the recognition and classification of each protozoan and metazoan from its morphological parameters.

Primary Treatment

Settlers Aeration

Tanks

Digesters

Sludge Dehydration

Tempest Bassins



2.1.2.2 PROTOZOA AND METAZOA IMAGE ACQUISITION AND PROCESSING

The protozoa and metazoa sample collection was performed by Maurício da Motta as part of his PhD work and is fully detailed in da Motta (2001). Image acquisition of the protozoa and metazoa in Nancy was performed through the visualization on a Leitz Dialux 20 microscope (Leitz, Wetzlar) coupled to a Hitachi CCTV HV-720E(F) (Hitachi, Tokyo) grey scale video camera. Computer image grabbing was performed in 8 bit (256 colours) 768x576 pixels matrix by a Matrox Meteor (Matrox, Montreal) frame grabber using the commercial software package Visilog (Noesis, S.A., Les Ulis).

Image acquisition of the protozoa and metazoa in Braga was performed through the visualization on a Zeiss Axioscop microscope (Zeiss, Oberkochen) coupled to a Sony CCD AVC–D5CE (Sony, Tokyo) grey scale video camera. Computer image grabbing was performed in 8 bit (256 grey levels) 768x576 pixels matrix by a Data Translation DT 3155 (Data Translation, Marlboro) frame grabber using the commercial software package Image Pro Plus (Media Cybernetics, Silver Spring).

All the samples were collected from the aerated tanks of the above mentioned plants and, in all cases, the maximum period of time between sample collection and image acquisition did not exceed 3 hours with no more than half an hour without aeration. Droplets of the samples were taken to a slide and covered with a cover slip for visualization and image acquisition in direct light microscope. The total magnification used for the protozoa and metazoa were the following: Aelosoma sp. (25 and 100 times); Nematoda, (100 and 250 times); Digononta, Monogononta, Arcella sp. and Euglypha sp. (250 and 400 times); A. cicada, Carchesium sp., Epistylis sp., Euplotes sp., Litonotus sp., Opercularia sp., Peranema sp., Suctoria, Trachellophyllum sp., Trithigmostoma sp., Trochilia sp., V. aquadulcis, V. microstoma, Vorticella and Zoothamnium sp. (400 times). Optimal focusing was sought in order to enhance the cilia, flagella and stalks (when present) as well as protozoa and metazoa boundaries. The metric unit dimensions for each magnification were further calibrated to pixels with the help of a micrometer slide.

The image analysis and processing for the protozoa and metazoa was achieved by means of specially developed programme in Visilog (Noesis, S.A., les Ulis) called ProtoRec which is detailed further on in Section 2.3.



2.1.3 ANAEROBIC GRANULATION PROCESS MONITORING 2.1.3.1 ANAEROBIC GRANULATION EXPERIMENTAL SURVEY

For this anaerobic granulation process survey, an expanded granular sludge blanket (EGSB) reactor was used, with a volume of 11.47 L, 2.22 m high and a height to diameter ratio of 27, in order to achieve both high superficial velocities and wide ranges of Hydraulic Retention Times. Figure 2.4 presents a scheme of the experimental set-up. The operation of this reactor is part of the PhD thesis of Pablo Araya-Kroff and is fully detailed in Araya-Kroff et al. (2002).

The inoculum was obtained from a sludge digester of Parada wastewater treatment plant, previously screened by a 0.7 mm sieve and presenting a Volatile Suspended Solids (VSS) content of 13.5 g VSS/L in a total sludge inoculum volume of 5 L. This reactor was operated at 34-37 ºC with a feed rate ranging from 2.6 L/d up to 48.5 L/d, a recycling rate ranging from 64 L/d to up to 363 L/d and Hydraulic Retention Time ranging from 106.2 h down to 5.7 h respectively in the beginning and the end of the experience.

Figure 2.4 – Experimental Set-Up: a) EGSB. b) Internal settler. c) External settler. d) Biogas flow-meter. e) Recycle f) Feed containers (adapted from Araya-Kroff et al., 2002).

The alkalinity sources were provided by sodium bicarbonate and calcium carbonate and micro and macronutrients were added as described elsewhere (Alves et al, 2001). A low acetate concentration was chosen, in order to favour the selection of Methanosaeta-like organisms (Wiegant and de Man, 1985).

The reactor operation was divided in three operating stages. The first stage lasted from day 1 until day 79 when an irremediable problem in the reactor took place which resulted in a total sludge loss of around 2 L. Therefore, a second 2 L inoculum from a sludge digester of Viana do Castelo wastewater treatment plant was added and the granulation process restarted for the period between day 110 to 207. After this second stage the biomass was collected and stored in a refrigerator at 4 ºC for a period of a

a

b

d

c

e



month. Finally, the third stage lasted from day 237 until the end of the granulation experiment at day 399.

During all the operation period the reactor was fed with a 1.5 g COD/L synthetic feed. In the first two stages of the operation the feed was composed of glucose (60% COD), acetic acid (28% COD) and propionic acid (12% COD) to ensure the growth of acidogenic populations. From day 326 until the end of the experiment glucose concentration was lowered to 30% COD and acetic acid and propionic acid rose to 49% and 21% COD respectively.

In the first two stages, the superficial Up-Flow Velocity was raised from 0.75 m/h up to 3.5 m/h low enough to prevent the wash-out of growing granulation nuclei, but high enough to maintain a mixing pattern that promoted the contact between the aggregates and at Organic Loading Rate ranging from 0.5 g COD/L.d up to 1.5 g COD/L.d. During the third stage, the reactor was operated at Organic Loading Rate up to 6.5 g COD/L.day) and higher liquid Up-Flow Velocity (up to 5.5 m/h) in order to prevent possible growth limitation of the biomass and to maintain a strong hydraulic selection pressure over the biomass, respectively.

The purpose of this study focused on the enlightenment of the filamentous contents and granular biomass morphological properties changes occurring during the granulation process of the biomass present in an anaerobic digester. All the following operating parameters were provided by Pablo Araya-Kroff and Lúcia Neves and are fully detailed in Araya-Kroff et al. (2002) and Neves (2002): Volatile Suspended Solids, Hydraulic Retention Time, Organic Loading Rate and Up-flow Velocity were determined by the methods described in Section 2.2.2. Three image analysis programmes were then created in Matlab (The Mathworks, Inc., Natick) to provide the following data: Aggregate Area, Total Aggregates Area, Equivalent Diameter, Perimeter, Width, Length, Shape Factor, Eccentricity, Convexity, Compactness, Solidity, Roundness, Extent, Mass Fractal Dimension, Surface Fractal Dimension, Mass Ratio Fractal Dimension, Area vs. Perimeter Fractal Dimension, Area vs. Feret Diameter Fractal Dimension and Perimeter vs. Feret Diameter Fractal Dimension in terms of aggregates morphology and Filament Length, Total Filaments Length per Total Aggregates Area Ratio and Total Filaments Length per Volatile Suspended Solids in terms of filamentous bacteria contents (all of these parameters are further described in Section 2.4).

2.1.3.2 ANAEROBIC GRANULATION IMAGE ACQUISITION AND PROCESSING

Filament image acquisition was accomplished through phase contrast microscopy on a Diaphot 300 Nikon microscope (Nikon Corporation, Tokyo) with a 100 times magnification. Aggregates larger than 0.1 mm in equivalent diameter were acquired through visualisation on an Olympus SZ 40 stereo microscope (Olympus, Tokyo) with a 40 times magnification. Aggregates smaller than 0.1 mm in equivalent diameter were acquired through visualisation on a Zeiss Axioscop microscope (Zeiss, Oberkochen) with a 100 times magnification. All the images were digitised with the help of a CCD AVC D5CE Sony grey scale video camera (Sony, Tokyo) and a DT 3155 Data Translation frame grabber (Data Translation, Marlboro), with a 768 x 576 pixel size in 8 bits (256 grey levels) by the Image Pro Plus (Media Cybernetics, Silver Spring) software package.



The sample collection was performed by Pablo Araya-Kroff as part of his PhD thesis and Lúcia Neves as part of her MSc thesis of and is fully detailed in Araya-Kroff et al. (2002) and Neves (2002). All the samples were collected by means of a plastic tube lowered from the top to the middle of the reactor with vacuum creation in the upper side of the tube entrapping thus a sample inside the tube. This procedure allowed for a representative sample of the reactor to be attained. For the filaments and smaller aggregates image acquisition, a volume of 35 µL was taken to a slide and covered with a 24x24 mm cover slip for visualization and image acquisition in phase contrast and direct light respectively, whereas for the larger aggregates image acquisition, a volume of 2.8 mL was taken to a Petri dish for visualization and image acquisition. For each of the filaments, and aggregates image acquisition around 60 images per sample were acquired in the beginning of the experiment evolving further on to around 100 images per sample.

Image acquisition of the smaller aggregates and filaments on the slide was obtained by three passages at one quarter, half and three quarters of the slide, as described in 2.1.1.2, and optimal focusing was then sought in order to enhance the aggregates borders and the filaments respectively. With respect to the larger aggregates, the Petri dish was thoroughly screened, as shown in Figure 2.5, from left to right and top to bottom with optimal focusing in the aggregates borders. The metric unit dimensions for each magnification were further calibrated to pixels with the help of a micrometer slide.

Figure 2.5 – Image acquisition methodology within each Petri dish.

The image analysis and processing for the aggregates and filaments was achieved by means of specially developed programmes in Matlab (The Mathworks, Inc., Natick). For the aggregates with an equivalent diameter respectively inferior and superior to 0.1 mm the programmes called Flocs and Granules were used whereas for the filamentous bacteria the programme Filaments was used. All of these programmes are detailed further on in Section 2.3.



2.1.4 GRANULE DETERIORATION TRIGGERED BY OLEIC ACID 2.1.4.1 GRANULE DETERIORATION EXPERIMENTAL SURVEY

In the study of the oleic acid triggered anaerobic granules deterioration, an Expanded Granular Sludge Bed (EGSB) reactor with granular sludge was fed with a synthetic effluent containing oleic acid as the sole carbon source and biomass recirculation. The operation of this reactor is part of the PhD thesis of Alcina Pereira and is fully detailed in Pereira et al. (2001).

The EGSB digester is shown in Figure 2.6 and consisted of a 10 L digester inoculated with 1.6 L of granular sludge (20.2 g VSS/L) from an UASB treating a UNICER, S.A. (Porto) brewery effluent. This reactor was operated at 37 ºC with increasing oleic acid concentrations between 1.9 and 8.2 g COD/L, a feed rate of 10 L/d and a recycling rate of 14 L/d at a constant Hydraulic Retention Time of 1 day.

This experiment was divided in four different periods accordingly to the feed composition: during the first 70 days the substrate was made of skim milk (50% COD) and oleic acid (50% COD) in a total 3.8 g COD/L; from day 70 to day 119 oleic acid was fed at a concentration of 3.8 g COD/L, as the sole carbon source; from day 119 to day 162 oleic acid was fed at a concentration of 6.2 g COD/L, as the sole carbon source and finally from day 162 to day 219 oleic acid was fed at a concentration of 8.2 g COD/L, as the sole carbon source. Macro and micronutrients with a composition described elsewhere (Alves et al., 2001) were added. The Organic Loading Rate ranged from 1.9 g COD/L.d in the beginning of the operation up to 8.2 g COD/L.d at the end with a constant Up-Flow Velocity of 0.2 m/h.

The biomass was segregated in two layers, a bottom layer where a little amount of substrate was adsorbed, and the top layer, which exhibited large amounts of adsorbed substrate.

Figure 2.6 – Experimental Set-Up. a) EGSB b) Internal settler c) Feeding d) Biogas flow-meter d) External settler e) Recycle f) Treated effluent.

a

b

c

d e

f



The purpose of this study focused on the enlightenment of the filamentous contents and granular biomass morphological properties changes occurring in anaerobic granule deterioration processes by increasing oleic acid concentrations. All the following operating parameters were provided by Alcina Pereira and are fully detailed in Pereira et al. (2001): Volatile Suspended Solids, Hydraulic Retention Time, Organic Loading Rate, Up-flow Velocity and Fines Weight Percentage were determined by the methods described in Section 2.2.2. Two image analysis programmes were then created in Matlab (The Mathworks, Inc., Natick) to provide the following data: Aggregate Area, Total Aggregates Area, Fines Area Percentage, Equivalent Diameter, Perimeter, Width, Length, Shape Factor, Eccentricity, Convexity, Compactness, Solidity, Roundness, Extent, Mass Fractal Dimension, Surface Fractal Dimension, Mass ratio Fractal Dimension, Area vs. Perimeter Fractal Dimension, Area vs. Feret Diameter Fractal Dimension and Perimeter vs. Feret Diameter Fractal Dimension in terms of aggregates morphology and Filament Length, Total Filaments Length per Total Aggregates Area Ratio and Total Filaments Length per Volatile Suspended Solids in terms of filamentous bacteria contents (all of these parameters are further described in Section 2.4).

2.1.4.2 GRANULE DETERIORATION IMAGE ACQUISITION AND PROCESSING

Filament image acquisition was accomplished through phase contrast microscopy on a Diaphot 300 Nikon microscope (Nikon Corporation, Tokyo) with a 100 times magnification. Aggregates larger than 0.05 mm in equivalent diameter were acquired through visualisation on an Olympus SZ 40 stereo microscope (Olympus, Tokyo) with a 15 times magnification. All the images were digitised with the help of a CCD AVC D5CE Sony grey scale video camera (Sony, Tokyo) and a DT 3155 Data Translation frame grabber (Data Translation, Marlboro), with a 768 x 576 pixel size in 8 bits (256 grey levels) by the Image Pro Plus (Media Cybernetics, Silver Spring) software package.

Two sets of samples were collected from the sample collectors of the top section and bottom section of the EGSB, and the overall composition within the whole reactor was estimated from these samples.

For the filaments image acquisition, a volume of 20 µL was taken to a slide and covered with a 20x20 mm cover slip for visualization and image acquisition in phase contrast and direct light respectively, whereas for the aggregates image acquisition, a volume of 2.8 mL was taken to a Petri dish for visualization and image acquisition. For each of the filaments, and aggregates image acquisition around 50 images per sample were acquired.

Image acquisition of the filaments on the slide was obtained by three passages at one quarter, half and three quarters of the slide, as described in 2.1.1.2, and optimal focusing was then sought in order to enhance the filaments. With respect to the aggregates the Petri dish was thoroughly screened, as described in 2.1.3.2 from left to right and top to bottom with optimal focusing in the aggregates borders. The metric unit dimensions for each magnification were further calibrated to pixels with the help of a micrometer slide.

The image analysis and processing for the aggregates and filaments was achieved by means of specially developed programmes in Matlab (The Mathworks, Inc., Natick). For the aggregates the programme called Flocs was used whereas for the filamentous bacteria the programme Filaments was used. All of these programmes are detailed further on in Section 2.3.

Materials and Methods Operating parameters 2.2


2.2 OPERATING PARAMETERS

In this section the operating parameters Total Suspended Solids, Hydraulic Retention Time, Sludge Volume Index and Diluted Sludge Volume Index made for the activated sludge∗ characterization are described.

Also the operating parameters Chemical Oxygen Demand, Volatile Suspended Solids, Hydraulic Retention Time, Organic Loading Rate and Up-flow Velocity made for the anaerobic sludge∗∗ characterization are described.

∗ All the operating parameters were provided by Frossos wastewater treatment plant

laboratory and are fully detailed in Rodrigues (2000). ∗∗ The operating parameters in the granulation process were provided by Pablo Araya-

Kroff and Lúcia Neves and are fully detailed in Araya-Kroff et al. (2002) and Neves (2002). The operating parameters in the granule deterioration were provided by Alcina Pereira and are fully detailed in Pereira et al. (2001).

Materials and Methods 2.2 Operating parameters


2.2.1 ACTIVATED SLUDGE OPERATING PARAMETERS

The operating parameters made for the activated sludge characterization are next briefly described. A more comprehensive description of the techniques can be obtained in the given references.

The Total Suspended Solids (TSS) were determined according to Tchobanoglous and Burton (2003).

Hydraulic Retention Time (HRT)

The Hydraulic Retention Time is the average time spent by the effluent liquid in the aeration tank and is determined by (Tchobanoglous and Burton, 2003):

F

VHRTQ

= (2.1)

Where V is the Volume of both the aeration and settling tanks and QF the Feed Flow Rate of the incoming effluent.

Sludge Volume Index (SVI)

Sludge settleability properties were determined by the calculation of the Sludge Volume Index given by (APHA et al., 1989):

30

0 hSVI

h TSS= (2.2)

Where h0 is the Settled Sludge Height after 0 minutes of settling time and h30 is the Settled Sludge Height after 30 minutes of sedimentation time.

Diluted Sludge Volume Index (SVID)

For highly concentrated mixed liquors (h30/h0 > 0.25 or h30 > 200 mL) it is preferable to use the diluted Sludge Volume Index:

30

0

D

D DilD

hSVI fh TSS

= (2.3)

Where 0Dh is the Settled Diluted Sludge Height after 0 minutes of settling time, 30

Dh is the Settled Diluted Sludge Height after 30 minutes and fDil is the dilution factor.

Materials and Methods Operating parameters 2.2


2.2.2 ANAEROBIC DIGESTION OPERATING PARAMETERS

The operating parameters made for the anaerobic sludge characterization are next briefly described. A more comprehensive description of the techniques can be consulted in the given references.

The Chemical Oxygen Demand (COD) was determined according to APHA et al. (1989) and the Volatile Suspended Solids (VSS) according to Tchobanoglous and Burton (2003). The Hydraulic Retention Time (HRT) was determined in a similar way as for the activated sludge.

Organic Loading Rate (OLR)

The Organic Loading Rate is the COD flow rate and is given by (Tchobanoglous and Burton, 2003):

CODOLRHRT

= (2.4)

Up-flow Velocity (vUF)

The Up-Flow Velocity is the velocity of the liquid inside the digester and is given by (Tchobanoglous and Burton, 2003):

F RUF

Q QvS+

= (2.5)

Where S is the Section Area of the digester, QF the Feed Flow Rate and QR the Recycle Flow Rate.

Fines Weight Percentage (FW %)

The Fines Weight Percentage is the weight percentage of the Fines, i.e., the aggregates that were removed by a syringe eqquiped with a 20 Gx1” needle (1 mm in diameter). This parameter is given by the ratio between the sum of the weight of the Fines aggregates and the Aggregates Total Weight (Amaral et al., 2001a):

1 %

Finesn

ii

WFW

TW==∑

(2.6)

where Wi is the Weight of each i Fine aggregate and TW is the Aggregates Total Weight is the weight sum of all the aggregates.

Materials and Methods 2.2 Operating parameters


Materials and Methods Image Processing 2.3


2.3 IMAGE PROCESSING

In this section a full description of the specially designed programmes is provided emphasizing the different stages within each of the four programmes: ProtoRec* for the protozoa and metazoa, Flocs for the smaller aggregates, Granules for the larger aggregates and Filaments for the filamentous bacteria.

A schematic representation and the resulting images from the main steps of the programmes are also provided at the end of each programme description.

* This programme was developed by a joint effort of a portuguese team from Biological

Engineering Centre of University of Minho headed by Eugénio Ferreira and a french team of Laboratoire des Sciences du Génie Chimique (CNRS-ENSIC-INPL-LSGC) of Nancy headed by Marie-Nöelle Pons.

Materials and Methods 2.3 Image Processing


2.3.1 PROTOZOA AND METAZOA IMAGE PROCESSING

The software developed for this study (ProtoRec v.3) was programmed in Visilog (Noesis, S.A., les Ulis) by a joint effort of a portuguese team from Biological Engineering Centre of University of Minho headed by Eugénio Ferreira and a french team of Laboratoire des Sciences du Génie Chimique (CNRS-ENSIC-INPL-LSGC) of Nancy headed by Marie-Nöelle Pons. This programme consists of 2 sub-routines that can be used together or separately. The first of the sub-routines (ProtTrat_m) is an image processing semi automatic programme to obtain the binary images of the protozoa and metazoa from the original 256 grey level images. The second (ProtData_s) is a fully automatic image analysis programme to determine and save the morphological parameters of the protozoa and metazoa. At the end of this section Figure 2.11 and Figure 2.12 summarize the main steps of the ProtoRec image analysis and processing programmes.

The main stages of these programs are as follows: Pre-treatment Definition of Region-of-Interest (ROI) Segmentation Debris deletion Labelling Determination of the morphological parameters Registering

Pre-treatment

The first step of the ProtoRec programme resides on the improvement of the 256 grey levels original image. In this stage the image is first equalized in order to enhance the contrast of the boundaries of the protozoa and metazoa and subsequently filtered by a median filter so that pixel sized differences can be softened. Following linear equalization and a maximum between this image and the median one allows for a better background removal. This stage consists of the following steps:

1. Image acquisition

Image acquisition is performed in TIFF format with 256 grey levels (8 bit).

2. Histogram equalization

Histogram equalization of the original image in order to increase contrast. If an image contains N pixels that use M grey levels there is a N/M pixels average which, should be the number of pixels for each grey level in a correctly balanced image. Accordingly, for each m level, in the original image, the new m’ value of the resulting image is determined by (Gonzalez and Woods, 1992):

0'

Mm

m

NmN=

= ∑ (2.7)



where Nm is the number of m intensity pixels and N the total number of pixels.

3. Median filtering (box size 2)

Filtering with a 2x2 box size median filtering in order to soften the image. The median filter is a non-linear filter that substitutes the value of each pixel by the median value of a pre-defined box around it (Russ, 1995).

4. Local equalization (box size 16)

Local equalization with a 16x16 box size. Consists of a histogram equalization performed to each pixel in a pre-defined box around it (Gonzalez and Woods, 1992).

5. Maximum between images

Maximum of each pixel from the last image and the image resulting from point 3.

6. User parameters dialog box

Opening of a dialog box, shown in Figure 2.7, with the following user defined parameters:

Working folder (folder with the original images). Segmentation method (entropy, automatic threshold of 127 or manual

threshold method). The default method is the manual threshold method. Maximum erosion value (function of the size of the protozoa and metazoa).

The default value is 15. Number of protozoa and metazoa in an image. The default value is 1.

Figure 2.7 – User parameters dialog box.



Definition of Region-of-Interest (ROI)

This stage consists of defining a Region-of-Interest, i. e., the region where the protozoa(n) or metazoa(n) are found in the grey level image. The first step of this stage is the definition of a user-chosen polygon. Secondly, the polygon needs to be dilated in order to be filled and immediately eroded to attain the initial size. This binary image is then used as a mask for cropping the ROI in the grey level image. Finally, a white background cropped image is obtained with the binary image inversion and addition to the cropped image. This stage consists of the following steps:

7. Identification of the ROI

The user chooses, with the aid of the mouse, the region where the protozoa(n) or metazoa(n) is located, as shown in Figure 2.8.

Figure 2.8 – Region of Interest (ROI) dialog box.

8. Dilation (order 1)

The morphological binary dilation operation on the user defined polygon image substitutes each pixel value by the higher value around it. The order of the dilation defines the number of times this operation is performed (Gonzalez and Woods, 1992). A first order dilation is performed in this step.

9. ROI polygon filling

The inner polygon is filled by an image filling function. This function first determines the inverse image and subsequently carries out a geodesic dilation. This function consists on performing successive dilations where each of them is followed by an intersection with the first image until convergence which guaranties that the dilated objects remain separated. Finally the final image is inverted to obtain the inner polygon filled (Noesis, 1998).



10. Erosion (order 2)

The morphological binary erosion operation substitutes each pixel value by the lowest value around it. The order of the erosion defines the number of times this operation is performed (Gonzalez and Woods, 1992). A second order erosion is performed I this step.

11. Mask cropping

This last binary image is used as a mask in the image resulting from point 5. This technique consists on a pixel-to-pixel multiplication of a grey scale image by a binary one called the mask image. Only the pixels in the original image corresponding to white pixels in the mask image retain their values while all the others become 0 (Russ, 1995).

12. Mask image inversion

The inverse of an image is another image where each pixel assumes the inverse value in relation to the extreme values of the image.

13. Image addition

Pixel-to-pixel image addition of the last image and the image resulting from point 11.

Segmentation

This stage consists primarily in the segmentation of the protozoa and metazoa boundaries through the use of a given threshold value as shown in Figure 2.9. After this step where the 256 grey level image transforms into a binary image (objects – 1; background – 0) morphological erosion and reconstruction are applied in order to remove small debris. Finally, a morphological closing is performed so that small gaps in the objects boundaries can be eliminated and the object filled. This stage consists of the following steps:

14. Segmentation

Segmentation by a given threshold where the objects pixels assume value 1 and the background value 0. The available options in the programme are: entropy or 127-level automatic methods or manual threshold method.

In the 127-level automatic method the programme assumes a threshold of 127.

In the manual threshold method the user defines the appropriate threshold value.

In the entropy automatic method the programme maximizes the total entropy as follows (Pons and Vivier, 1999):

Let it be pm the probability for each pixel to have an m grey level:



mm

NpN

= (2.8)

where Ni is the number of i intensity pixels and N the total number of pixels.

Regarding the case of segmenting into two regions (w0 and w1) the probability of each pixel to have a grey level respectively equal or less than j or higher than j is:

00

j

mm

w p=

= ∑ (2.9)

255

11

mm j

w p= +

= ∑ (2.10)

The total entropy is given by:

φ φ φ= +0 1 (2.11)

where φ 0 and φ 1 are respectively the entropies of the w0 and w1 regions:

00 0 0

logj

m m

m

p pw w

φ=

=

∑ (2.12)

255

11 1 1

logm m

m k

p pw w

φ= +

=

∑ (2.13)

Figure 2.9 – Segmentation dialog box.


A second order erosion is performed.



16. Reconstruction

The reconstruction is performed with the last image on the image resulting from point 14. The reconstruction operation allows for the recognition of all the objects in an image with markers in a so-called marker image (Noesis, 1998).

17. Closing (order 1)

The morphological closing operation consists on a dilation followed by erosion. The order of the closing defines the number of times these operations are performed (Gonzalez and Woods, 1992).

18. Image filling

The inner aggregates holes are filled by an image filling function.

Debris Deletion

The morphological erosion operation is applied followed by a reconstruction in order to remove large size debris. This stage consists of the following steps:

19. Erosion

The order of the erosion is given by the maximum erosion value (point 6).

20. Reconstruction

The reconstruction is performed with the last image on the image resulting from point 18.

Labelling

The last stage of the ProtoRec image processing programme consists in the labelling of all the protozoa and metazoa found in the final binary image.

21. Labelling

In this step all of the pixels belonging to each different protozoa(n) or metazoa(n) present in the image are given the same discrete number differing only from each protozoa(n) or metazoa(n).

Determination of the Morphological Parameters

The first stage of the ProtoRec image analysis programme resides on the determination and removal of the protozoa stalk (if found) based on the following steps:

Determination of the maximum number of erosions ERSMax. This number may vary from half of the highest value found in the distance image (or Euclidean Distance Map) of the protozoan or metazoan up to a maximum of 26 (enough to



remove any stalk). The Euclidean Distance Map (EDM) of an image substitutes each pixel within an object to the value of its distance to the nearest object boundary and is shown in Figure 2.10.

Determination of the real number of erosions needed to remove the stalk through the calculation of the RTer ratio. This ratio should be higher than 103 and is determined by the following equation:

5010050

er Maxer

ant

A ERSRTA

−= + (2.14)

where Aer is the Area of the object at a given number of erosions er and Aant is the previous erosion Area.

Removal of the protozoan stalk by eroding the lowest erosion number that satisfies the above criteria.

Recovery of the stalk as the largest separated object from the protozoan body in the previous step.

Figure 2.10 – Euclidean distance map of an object.

The parameters determined by this programme are the following: Area, Total Area, Equivalent Diameter, Perimeter, Length, Width, Feret Shape, Eccentricity, Shape Factor, Robustness, Concavity Index, Concavity Ratio, Convexity, Compactness, Solidity, Average Width, Perimeter Factor, Stalk Length, Stalk Average Width, WSWBA Ratio, WBAWB Ratio and Euclidean Distance Map Fractal Dimension. With the exception of the parameters Perimeter Factor, Stalk Medium Width and WSWBA Ratio all the other parameters were determined for both the whole of the protozoan or metazoan and for their body only (without the stalk). All of theses parameters are thoroughly described in Section 2.4.

Registering

The last stage of both the programmes in ProtoRec resides on saving the final image of the protozoa and metazoa as well as the morphological parameters file.

22. Registering the image

The final labelled protozoa and metazoa image is saved in 8 bit TIFF format.



23. Registering the results

The morphological parameters are saved in an ASCII (American Standard Code for Information Interchange) format file.

A schematic representation of the ProtoRec programme is shown in Figure 2.11 and Figure 2.12 summarizes the main steps of the ProtoRec image processing programme.



Figure 2.11 – Schematic representation of ProtoRec programme.

Image acquisition

Segmentation

Mask Image Inversion

Mask Cropping

Erosion (2)

ROI Polygon Filling

Dilation (1)

ROI Identification

Dialog box

Maximum Local Histogram Equalization (16x16)

Median Filtering (2x2)

Histogram Equalization

Addition

Registering

Labelling

Reconstruction Erosion (2)

Reconstruction Erosion

Image Filling

Closing (1)

Morphological Parameters

Registering



Figure 2.12 – Resulting images from the main steps of the ProtoRec programme (the numbers in

brackets refer to the step number).

[1] [2] [3]

[4] [5] [8]

[9]

[20] [19]

[18] [17] [16]

[15] [14] [13]

[11] [10]

[21]



2.3.2 FLOCS IMAGE PROCESSING

The developed programme (Flocs) in Matlab (The MathWorks, Inc., Natick) was programmed in Biological Engineering Centre of University of Minho. This programme is an image processing and analysis automatic programme to obtain the binary images of the aggregates from the original 256 grey level images and determine and save the morphological parameters of the aggregates. At the end of this section Figure 2.14 and Figure 2.15 summarize the main steps of the Flocs image analysis and processing programme.

The main stages of these programs are as follows: User definitions Pre-treatment Segmentation Debris deletion Labelling Determination of the morphological parameters Registering

User Definitions

The first step of the Flocs programme consists on displaying a dialog box for the user to define some processing parameters.



Identification of the image processing mode: image by image or set of images. Working folder (folder with the original images). Parameters selection: whole set of the parameters or area only. Segmentation threshold level. The default threshold is 0.2. Aggregates minimum size. The default value is 9 pixels. Pixel to millimetre calibration. The default value is 1.




Pre-Treatment

The first step of the Flocs programme resides on the improvement of the 256 grey level original image. In this stage a background image is used to eliminate the background light differences from the image, then equalized in order to enhance the contrast of the boundaries of the aggregates and subsequently filtered by a wiener filter so that pixel sized differences can be softened. This stage consists of the following steps:


Image acquisition is performed in TIFF or BMP format with 256 grey levels (8 bit).

3. Background correction

The original image is divided by a background image with the purpose of eliminating background light differences from the image.

4. Histogram equalization

Histogram equalization is then performed to the image in order to increase its contrast.

5. Wiener filtering

Filtering with a lowpass Wiener filter in order to soften the image. The Wiener filter uses a pixel-wise adaptive Wiener method based on statistics estimated from a local neighbourhood of each pixel (Glasbey and Horgan, 1995).



Segmentation

This stage consists primarily in the segmentation of the aggregates by the simultaneous use of boundary image segmentation and percentile based region image segmentation and the conjugation of the resulting images. After this step the 256 grey level image is transformed into a binary image (objects – 1; background – 0). Prior to the segmentation step a boundary image was obtained by the use of morphological operations. This stage consists of the following steps:

6. Grey scale erosion (order 1)

The morphological grey scale erosion operation substitutes each pixel value by the lowest value around it. The order of the erosion defines the number of times this operation is performed (Gonzalez and Woods, 1992).

7. Grey scale dilation (order 1)

A grey scaled dilation is performed on the image from point 5. The morphological grey scale dilation operation substitutes each pixel value by the highest value around it. The order of the dilation defines the number of times this operation is performed (Gonzalez and Woods, 1992).

8. Difference between images

The difference between the last image and the image resulting from point 6 is computed.

9. Segmentation

The segmentation operation is performed with a 0.075 threshold value.

10. Segmentation

The segmentation operation is performed on the image resulting from point 8 with the user defined threshold value.

11. Reconstruction

The reconstruction is performed with the image resulting from point 9 on the last image.

12. Image filling


13. Area ratio determination

The aggregates area ratio on the image resulting from point 12 is determined.



14. Percentile based threshold determination

A threshold value is then determined as the percentile with the same area ratio value on the image resulting from point 5.

15. Segmentation

The segmentation operation is performed on the image resulting from point 5 with the percentile based threshold.

16. Image conjugation

The morphological operator and is used in order to conjugate the last image and the image resulting from point 12. In this step only the common white pixels of each image remain in the final image.

Debris Deletion

Image filling and morphological operations such as erosion and reconstruction are used in order to identify and fulfil gaps inside the aggregates smaller than 6x6 pixels. Subsequently debris smaller than 9 pixels are removed and, finally the objects cut off by the image boundaries are removed. This stage consists of the following steps:

17. Image filling


18. Exclusion between images

The morphological operator exclusive or is used in the last image and the image resulting from point 16. In this step only the white pixels of the last image that are black in the image resulting from point 16 remain in the final image, identifying thus the aggregate gaps.


A third order erosion is performed.

20. Reconstruction



The morphological operator exclusive or is used in the last image and the image resulting from point 17. In this step only the white pixels of the image resulting from point 17 that are black in the last image remain in the final image.



22. Labelling

In this step all of the pixels belonging to each different aggregate present in the image are given the same discrete number differing only from aggregate to aggregate.

23. Area determination

The Area is calculated as the projected surface and is given by the number of pixels belonging to an object.

24. Debris elimination

The aggregates smaller than the user chosen aggregates minimum size are eliminated.

25. Border elimination

Aggregates cut off by the image boundaries are deleted

Labelling

The last stage of the Flocs image processing programme consists in the labelling of all the aggregates found in the binary image.

26. Labelling



The first stage of the Flocs image analysis programme resides on the determination of the morphological parameters of the aggregates.

The parameters determined by this programme are the following: Area, Total Area, Equivalent Diameter, Perimeter, Length, Width, Eccentricity, Shape Factor, Roundness, Extent, Convexity, Compactness, Solidity, Mass Fractal Dimension, Surface Fractal Dimension, Mass ratio Fractal Dimension, Area vs. Perimeter Fractal Dimension, Area vs. Feret diameter Fractal Dimension and Perimeter vs. Feret diameter Fractal Dimension. All of theses parameters are thoroughly described in Section 2.4.

Registering

The last stage of Flocs programme resides on saving the final image of the aggregates the morphological parameters file.




The final binary aggregates image is saved in TIFF format.


The morphological parameters are saved in a TXT (Text File) format file.

A schematic representation of the Flocs programme is shown in Figure 2.14 and Figure 2.15 summarizes the main steps of the Flocs image processing programme.



Figure 2.14 – Schematic representation of Flocs programme

User Parameters Dialog Box

Image Filling

Segmentation

Difference

Grey Scale Dilation (1) Grey Scale Erosion (1)

Wiener Filtering

Background Image Background Correction

Image Acquisition

Reconstruction


Percentile Based Threshold Determination

Area Ratio Determination

Exclusion Image Filling

Conjugation Segmentation

Segmentation

Histogram Equalization

Debris Elimination

Exclusion

Registering

Border elimination

Labelling Morphological Parameters

Registering

Labelling

Area Determination



Figure 2.15 – Resulting images from the main steps of the Flocs programme (the numbers in brackets refer to the step number).

[2] [3]

[8] [9]

[4]

[5]

[10] [11] [12]

[15] [16] [17]

[18] [19] [20]

[21] [22] [24]

[26] [25]



2.3.3 GRANULES IMAGE PROCESSING

The developed programme (Granules) in Matlab (The MathWorks, Inc., Natick) was programmed in Biological Engineering Centre of University of Minho. This programme is an image processing and analysis automatic programme to obtain the binary images of the aggregates from the original 256 grey level images and determine and save the morphological parameters of the aggregates. At the end of this section Figure 2.18 and Figure 2.19 summarize the main steps of the Granules image analysis and processing programme.


User Definitions

The first step of the Granules programme consists on displaying a dialog box for the user to define some processing parameters.



Identification of the image processing mode: image by image or set of images. Working folder (folder with the original images). Parameters selection: whole set of the parameters or area only. Segmentation method (histogram minima, histogram descent inflection point,

histogram ascent inflection point or manual threshold method). The default method is the manual threshold method.

Segmentation manual threshold level. The default threshold is 210. Aggregates minimum size. The default value is 9 pixels. Pixel to millimetre calibration. The default value is 1.




Pre-Treatment

The first step of the Granules programme resides on the improvement of the 256 grey level original image. In this stage a background image is used to eliminate the background light differences from the image, then a bottom hat filter is applied in order to obtain the boundaries of the aggregates and finally filtered by a wiener filter so that pixel sized differences can be softened. This stage consists of the following steps:





4. Grey scale bottom hat filtering (box size 6)

A grey scale bottom hat filtering is then performed to the image in order to obtain the boundaries of the aggregates. A bottom hat filter consists on first applying a closing operation with a mask defined by the box size and then subtracting the original image to the closed image.





6. Wiener filtering

Filtering with a lowpass Wiener filter in order to soften the image. The Wiener filter uses a pixel-wise adaptive Wiener method based on statistics estimated from a local neighbourhood of each pixel (Glasbey and Horgan, 1995).

Segmentation

This stage consists primarily in the segmentation of the aggregates by histogram minima, histogram descent inflection point, histogram ascent inflection point or a manual threshold level. Prior to the threshold determination step the image’s histogram was first obtain and smoothed by an averaging filter. After this step the 256 grey level image is transformed into a binary image (objects – 1; background – 0). This stage consists of the following steps:

7. Histogram determination

Determination of the image’s histogram in 256 values range.

8. Histogram smoothing

An averaging filter of range 20 was applied in the histogram in order to smooth small peaks.

9. Determination of the histogram first derivative minimum

The minimum between the two larger maxima of the histogram is determined and defined as the threshold level (see Figure 2.17).

0 64 128 192 256Grey level

Num

ber

Ascent InflectionPoint

Descent InflectionPoint

Minimum

Figure 2.17 – Histogram minima, ascent inflection point and descent inflection point.



10. Determination of the histogram descent inflection point

The steepest descent slope between the first of the two larger maxima and the minima of the histogram is determined and defined as the threshold level (see Figure 2.17).

11. Determination of the histogram ascent inflection point

The steepest ascent slope between the minima and the second of the two larger maxima of the histogram is determined and defined as the threshold level (see Figure 2.17).

12. Segmentation

The segmentation operation is performed with the defined threshold value.

Debris Deletion

Image filling and morphological operations such as erosion and reconstruction are used in order to identify and fulfil gaps inside the aggregates smaller than 6x6 pixels. Subsequently debris smaller than 9 pixels are removed and, finally the objects cut off by the image boundaries are removed. This stage consists of the following steps:

13. Image filling



The morphological operator exclusive or is used in the last image and the image resulting from point 12. In this step only the white pixels of the last image that are black in the image resulting from point 12 remain in the final image, identifying thus the aggregate gaps.


A third order erosion is performed.

16. Reconstruction



The morphological operator exclusive or is used in the last image and the image resulting from point 13. In this step only the white pixels of the image resulting from point 13 that are black in the last image remain in the final image.



18. Labelling





The aggregates smaller than the user chosen aggregates minimum size are eliminated.

21. Border elimination

Aggregates cut off by the image boundaries are deleted

Labelling

The last stage of the Granules image processing programme consists in the labelling of all the aggregates found in the binary image.

22. Labelling



The first stage of the Granules image analysis programme resides on the determination of the morphological parameters of the aggregates.

The parameters determined by this programme are the following: Area, Total Area, Equivalent Diameter, Perimeter, Length, Width, Eccentricity, Shape Factor, Roundness, Extent, Convexity, Compactness, Solidity, Mass Fractal Dimension, Surface Fractal Dimension, Mass ratio Fractal Dimension, Area vs. Perimeter Fractal Dimension, Area vs. Feret diameter Fractal Dimension and Perimeter vs. Feret diameter Fractal Dimension. All of theses parameters are thoroughly described in Section 2.4.

Registering

The last stage of Granules programme resides on saving the final image of the aggregates and the morphological parameters file.




The final binary aggregates image is saved in TIFF format.



A schematic representation of the Granules programme is shown in Figure 2.18.



Figure 2.18 – Schematic representation of Granules programme


Manual Threshold

Histogram

Difference

Ascent Inflection Point

Wiener Filtering


Image Acquisition

Minimum


Threshold Determination

Descent Inflection Point

Exclusion Image Filling

Segmentation

Average Filter (20)

Grey Scale Bottom Hat (6)

Labelling

Exclusion

Registering

Border elimination


Registering

Debris Elimination

Area Determination



Figure 2.19 summarizes the main steps of the Granules image processing programme.

Figure 2.19 – Resulting images from the main steps of the Granules programme (the numbers in


[2] [3] [4]

[5] [6] [12]

[13]

[17] [18]

[21] [22] [20]

[15] [14]

[16]



2.3.4 FILAMENTS IMAGE PROCESSING

The developed programme (Filaments) in Matlab (The MathWorks, Inc., Natick) was programmed in Biological Engineering Centre of University of Minho. This programme consists of 2 sub-routines that can be used together or separately. The first of the sub-routines (Filaments) is an image processing automatic programme to obtain the binary images of the filaments from the original 256 grey level images. The second (Filaments_param) is a fully automatic image analysis programme to determine and save the morphological parameters of the filaments. At the end of this section Figure 2.21 and Figure 2.22 summarize the main steps of the Filaments image analysis and processing programmes.


User Definitions

The first step of the Filaments programme consists on displaying a dialog box for the user to define some processing parameters.



Identification of the image processing mode: image by image or set of images. Working folder (folder with the original images). Filaments segmentation percentile level. The default percentile is 92.5. Filaments segmentation threshold level. The default threshold is 0.45. Aggregates segmentation threshold level. The default threshold is 0.25. Pixel to millimetre calibration. The default value is 1.




Pre-Treatment

The first step of the Filaments programme resides on the improvement of the 256 grey level original image. In this stage a background image is used to eliminate the background light differences from the image, re-shifted back to retain the original median, grey scale cut off and finally a bottom hat filter is applied in order to improve the filaments definition. This stage consists of the following steps:





4. Image values shift

The values of the background corrected image are then shifted back in order to retain the same median value as the original image.

5. Image grey scale cut off

The values superior to 2 are set to the value 2.



6. Grey scale bottom hat filtering (box size 4)

A grey scale bottom hat filtering is then performed to the image in order to improve the filaments definition.

Aggregates Segmentation

This stage consists primarily in the segmentation of the aggregates and elimination of these in the enhanced filaments image. Prior to the aggregates elimination a series of steps consisting on eliminating filaments and filament-like debris, enhancement and segmentation of aggregates. Finally, there is an aggregates gap filling, elimination of the aggregates smaller than 10x10 pixels and elimination of the debris within the recognized aggregates from the enhanced filaments image. This stage consists of the following steps:

7. Filaments and filament-like debris elimination

This operation is accomplished by the sum of the last image with the image from point 5.

8. Absolute of the difference of image to 0.5.

The difference between the image and the value 0.5 is computed and then the absolute values are determined.

9. Grey scale closing (order 10)

A grey scale closing operation is performed in order to enhance the aggregates.

10. Segmentation

The segmentation operation is performed with the aggregates threshold value.

11. Image filling



A tenth order erosion is performed.

13. Reconstruction




14. Mask operation

The last image is then applied as a mask to eliminate all the debris inside the recognized aggregates to the image from point 6.

Filaments Segmentation

This stage consists primarily in a pre-processing step followed by the segmentation of the filaments. The pre-processing step resides on the elimination of the pixels lower than the images percentile of the aggregates area to image area ratio and a final enhancing step and then the segmentation step is performed. This stage consists of the following steps:

15. Aggregates Area to Image Area Ratio (A/I) determination

The A/I Ratio is given by the ratio between the sum of all the Aggregates Area and the image area.

16. Percentile determination

The percentile of an A/I based formula on the filaments and filament-like debris image is determined.

17. Percentile based elimination

The pixels of the image from point 14 with lower values than the percentile value are set to zero.



19. Segmentation

The segmentation operation is performed with the filaments threshold value.

Debris Deletion

This step is done by the determination and use of a filaments marker image in the filaments and filament-like debris image. The determination of a second percentile value, images conjugation and a reconstruction operation are used for that purpose. This stage consists of the following steps:

20. Percentile determination

A second percentile based on both the user chosen percentile and the A/I Ratio on the image from point 15 is determined.



21. Segmentation

The segmentation operation of the image from point 18 is performed with the second percentile.

22. Image conjugation

The morphological operator and is used in order to conjugate the last image and the image resulting from point 19. The result of this operation is a filaments marker image.

23. Reconstruction

The reconstruction is performed with the image resulting from point 19 on the last image.

24. Labelling

In this step all of the pixels belonging to each different filament present in the image are given the same discrete number differing only from filament to filament.



26. Gyration radius determination

The gyration radius is determined as follows (Pons and Vivier, 1999):

2 22 X Y

Eq

M MGR

D+

= (2.15)

Where M2X and M2Y are the second order moments and M1X and M1Y are the first order moments given by:

11

1 Ni

X nn

M xA =

= ∑ (2.16)

11

1 Ni

Y nn

M yA =

= ∑ (2.17)

( )22 1

1

1 Ni

X n Xn

M x MA =

= −∑ (2.18)

( )22 1

1

1 Ni

Y n Yn

M y MA =

= −∑ (2.19)

And ( inx , i

ny ) represent the coordinates of each objects pixels.




All the objects that do not have an area larger than 32 pixels and a gyration radius larger than 1.2 are deleted.

Labelling

The last stage of the Filaments image processing programme consists in the labelling of all the filaments found in the binary image.

28. Labelling

In this step all of the pixels belonging to each different filament present in the image are given the same discrete number differing only from filament to filament.


The first stage of the Filaments image analysis programme resides on the determination of the morphological parameters of the filaments.

The parameters determined by this programme are the following: Filament Length, and Filaments Total Length. Theses parameters are thoroughly described in Section 2.4.

Registering

The last stage of both programmes in Filaments resides on saving the final image of the filaments and the morphological parameters file.


The final binary filaments image is saved in TIFF format.



A schematic representation of the Filaments programme is shown in Figure 2.21 and Figure 2.22 summarizes the main steps of the Filaments image processing programme.



Figure 2.21 – Schematic representation of Filaments programme


Segmentation

Debris Elimination

Image Values Shift

Percentile Based Elimination

Grey Scale Cut Off


Image Acquisition

Grey Scale Closing (10)

Image Filling

Conjugation

Percentile Determination Segmentation

Difference

Segmentation

Absolute of Difference

Grey Scale Bottom Hat (10)


Registering

Percentile Determination


Registering

A/I Determination

Mask Operation

Reconstruction

Area determination GR determination

Debris elimination



Figure 2.22 – Resulting images from the main steps of the Filaments programme (the numbers in


[2] [5]

[7]

[8] [9]

[10] [11]

[6]



(Continuation of Figure 2.22)

[12] [13]

[14] [17]

[18] [19]

[21] [22]



(Continuation of Figure 2.22)

[23] [24]

[27] [28]



Materials and Methods Morphological Parameters 2.4


2.4 MORPHOLOGICAL PARAMETERS

The morphological parameters Area, Total Area, Equivalent Diameter, Perimeter, Length, Width, Eccentricity, Shape Factor, Roundness, Extent, Convexity, Compactness, Solidity, Concavity Index, Concavity Ratio, Average Width, Perimeter Factor, Stalk Length, Stalk Average Width, WSWBA Ratio, WBAWB Ratio, Filament Length, and Filaments Total Length, as well as the fractal dimensions Mass Fractal Dimension, Surface Fractal Dimension, Mass ratio Fractal Dimension, Area vs. Perimeter Fractal Dimension, Area vs. Feret diameter Fractal Dimension, Perimeter vs. Feret diameter Fractal Dimension and Euclidean Distance Map Fractal Dimension are further described in this section

For both the image processing and the morphological parameters determination of the protozoa(n) and metazoa(n) identification Visilog software package was used whereas for the other studies of this work Matlab software package was employed.

Materials and Methods 2.4 Morphological Parameters


2.4.1 EUCLIDEAN MORPHOLOGICAL PARAMETERS

The Euclidean morphological parameters determined in the course of this work were as follows:

Number (Nb)

The Number of aggregates, flocs, filaments and granules is given by the identification and cumulative sum of each one excluding the ones cut off by the image boundaries.

Number Percentage (Number %)

The Number % of each size class is given by the ratio between the Class Total Number (TNClass) and the Total Number:

% ClassNbNumberNb

= (2.20)

where NbClass is the Total Number of the aggregates belonging to that class.

Area (A)

The Area is calculated as the projected object surface and is given by the number of pixels belonging to an object converted to metric units:

Obj CalA N F= × (2.21)

where NObj is the pixel sum of each object and FCal is the metric calibration factor.

Total Area (TA)

The Total Area is given by the cumulative Area of all the aggregates including the ones cut off by the image boundaries.

Area Percentage (Area %)

The Area % of each size class is given by the ratio between the sum of the Aggregates Area of that size class and the Total Area:

1 %

Classn

ii

AArea

TA==∑

(2.22)

where Ai is the Area of each i aggregate belonging to that size class.



Area Recognition Percentage (Rec %)

The Rec % is given by the ratio between the sum of all the Aggregates Area and the Total Area:

1 %

Objn

ii

ARec

TA==∑

(2.23)

where Ai is the Area of each i aggregate.

Fines Area Percentage (FA %)

The Fines Area Percentage is the area percentage of the Fines, i.e., the aggregates that were removed by a syringe eqquiped with a 20 Gx1” needle, that is, with a maximum Width of 1 mm as determined by image analysis. This parameter is given by the ratio between the sum of the Aggregates Area of the Fines aggregates and the the sum of all the Aggregates Area:

1

1

%

Fines

Obj

n

iin

jj

AFA

A

=

=

=∑

∑ (2.24)

where Ai is the Area of each i aggregate and Aj is the Area of each j Fine aggregate.

Equivalent Diameter (Deq)

The Equivalent Diameter (see Figure 2.23) of an object is given by the diameter of a circle of equal surface as the object converted to metric units (Russ, 1995).

2EqADπ

= (2.25)

Figure 2.23 – Representation of the projected image of an object and the morphological parameters Deq, FMax and FMin.

Perimeter (P)

The Perimeter was determined as the Crofton Perimeter of an object converted to metric units for the protozoa and metazoa identification. The Crofton perimeter is calculated as the average of the distances between parallel straight lines, at 8 different



angles, and the object boundaries (Noesis, 1998). For all the other works, the perimeter was determined by:

1.1222Per CalP N F= × × (2.26)

where NPer is the pixel sum of the objects boundary and the factor 1.1222 is used in order to homogenize the different angles of the filaments (Walsby and Avery, 1996).

Feret Diameter (FD)

The Feret Diameter of an object is the maximum distance between two parallel tangents touching opposite borders of the object (Glasbey and Horgan, 1995).

Width (W)

The Width of an object is given as the Minimum Feret Diameter FMin (see Figure 2.23) converted to metric units (Russ, 1995).

Length (FMax)

The Length of an object is given as the Maximum Feret Diameter FMax (see Figure 2.23) converted to metric units (Russ, 1995).

Filament Length (L)

Prior to the filament length determination the free filaments image was thinned to a 1 pixel width and pruned to eliminate fake branches connected to the filaments. Therefore, all the branches of the filaments inferior to 8 pixels were removed.

The filaments length was then determined by:

( ) 1.1222Thn Int CalL N N F= + × × (2.27)

where NThn is the pixel sum of each thinned filament, NInt is the number of filaments intersections and the factor 1.1222 is used in order to homogenize the different angles of the filaments (Walsby and Avery, 1996).

The free filaments are the filaments or filaments portions that are outside the aggregates, either attached to them or dispersed in the bulk, as shown in Figure 2.24.

Figure 2.24 – Representation of the free filaments.

Free Filaments



Filament Length per Aggregates Area Ratio (L/A)

Calculated as the ratio between the Filament Length and Aggregates Area:

/ LL AA

= (2.28)

Total Filament Length (TL)

The Total Filaments Length is given by the cumulative length of all the free filaments.

Total Filaments Length per Total Aggregates Area Ratio (TL/TA)

Calculated as the ratio between the Total Filaments Length and Total Aggregates Area:

/ TLTL TATA

= (2.29)

Total Filaments Length per Total Suspended Solids (TL/TSS)

Calculated as the ratio between the Total Filaments Length and Total Suspended Solids (TSS):

/ TLTL TSSTSS

= (2.30)

Feret Shape (FrSh)

The Feret Shape (or Feret Factor) is defined by the ratio between the Maximum Feret Diameter FMax and the Feret Diameter at 90º FMax90 of FMax (Noesis, 1998).

90

Max

Max

FFrShF

= (2.31)

Extent (Ext)

The Extent is defined by the ratio between the object area and its bounding box area (The Mathworks, Inc., 2002).

x BB BB

AExtW L

= (2.32)

where WBB is the Bounding Box Width and LBB the Bounding Box Length respectively as shown in Figure 2.25.



Figure 2.25 – Representation of the bounding box width and length.

Eccentricity (Ecc)

The Eccentricity is calculated by the 2nd order moments of the object (Glasbey and Horgan, 1995).

( ) ( )22 22 2 2

2

4 4X Y XYM M MEcc

Aπ − +

= (2.33)

where M2XY is the second horizontal and vertical order moment given by:

( )( )2 1 11

1 Ni i

XY n X n Yn

M x M y MA =

= − −∑ (2.34)

and ( inx , i

ny ) represent the coordinates of each objects pixels.

Shape Factor (ShF)

The Shape Factor (or Area-Perimeter Factor) is defined as follows (Noesis, 1998): 2

4PShF

Aπ= (2.35)

Convex Envelope

The Concavity, Robustness and Concavity Index morphological parameters depend on the creation of the object’s Convex Envelope which is the smaller convex polygon embracing the object, determined by the vectorization of the object contour. The vectorization process enables its representation as a series of straight lines each one limited by two contour points. Connecting all of this points and filling the resultant object gives the Convex Envelope of the object (Pons and Vivier, 1999).



Figure 2.26 – Representation of the Convex Envelope.

Robustness (Rob)

The Robustness is given by the following equation (Pons et al., 1997):

2 objerRob

A= (2.36)

where erobj is the number of erosions needed to delete an object.

Concavity Index (CI)

The Concavity Index is given by the following equation (Pons et al., 1997):

2 comperCI

A= (2.37)

where ercomp is the number of erosions needed to delete the complement of an object (in relation to its Convex Envelope).

Figure 2.27 – Representation of the complement of the object.

Concavity Ratio (CR)

Determined by the following equation (Pons et al., 1997): 24 comp

C

erCR

A A=

− (2.38)

where AC is the Convex Envelope Area.



Convexity (Conv)

Determined by the following equation (Glasbey and Horgan, 1995):

ConvPConvP

= (2.39)

where PConv is the Convex Envelope Perimeter (Glasbey and Horgan, 1995).

Roundness (Round)

Determined by the following equation (Glasbey and Horgan, 1995):

24

Conv

ARoundPπ

= (2.40)

Compactness (Comp)

Determined by the following equation (Russ, 1995):

4

Max

AComp

Fπ= (2.41)

Solidity (Sol)

The Solidity is calculated as the ratio between the object Area and the Convex Envelope Area (Russ, 1995).

C

ASolA

= (2.42)

Average Width (WA)

Determined as the ratio between the object Area and Length (protozoa and metazoa only):

AAWL

= (2.43)

Perimeter Factor (PF)

Determined by the following equation:

PPPFP

= (2.44)

where PP is the Deleted Stalk Perimeter, i. e., the object Perimeter after stalk removal (protozoa and metazoa only).



Stalk Length (LStk)

Determined by the following equation:

2

42 2

2

Stk StkStk

Stk

P P AL

+ − = (2.45)

where the Stalk Perimeter (PStk) is determined as the Perimeter of the stalk and Stalk Area (AStk) as the Area of the stalk (protozoa and metazoa only).

Stalk Average Width (WStk)

Determined as the ratio between the Stalk Area and Stalk Length (LStk) (protozoa and metazoa only):

StkStk

Stk

AWL

= (2.46)

Stalk Average Width per Body Average Width Ratio (WSWBA)

Calculated as the ratio between the Stalk Average Width and Body Average Width (WBA):

StkS BA

BA

WW WW

= (2.47)

where the Body Average Width (WBA) is determined as the Average Width for the body of the object (protozoa and metazoa only).

Body Average Width per Body Width Ratio (WBAWB)

Determined as the ratio between the Body Average Width and Body Width (WB):

BABA B

B

WW WW

= (2.48)

where the Body Width (WB) is determined as the Width for the body of the object (protozoa and metazoa only).



2.4.2 FRACTAL DIMENSIONS

The inability of conventional Euclidean geometry to describe complex natural shapes led to the foundation of the so-called fractal dimensions. Traditionally, the dimensions used to describe the world structure were: 0 (points); 1 (lines); 2 (surfaces); 3 (space) and a theoretically 4th dimension (time). However, Besicovitch expanding the work of Hausdorff, proposed that natural shapes could have fractioned dimensions such as 1.5 or 2.3 for instance. And, as a matter of fact, fractal geometry (Mandelbrot, 1977), and mainly fractal dimensions, have found a large field of applications in all kinds of biological systems and natural irregular structures.

Fractal dimension is a generic term without a strict definition enclosing a large set of different, yet related, parameters reflecting the complexity of a given structure, that is, the convulsions and irregularities of the structure contour. Fractal dimensions values are, however, largely dependent on several factors: sampling, rounding, resolution and even the value of the fractal dimension itself.

Several methods are available to determine the fractal dimension of any structure, such as the Liebovitch box counting algorithm (Liebovitch and Toth, 1989), variation method (Huang et al., 1994), cross-correlation function (Hermanowicz et al., 1995 and 1996), calliper dimensions determination (Russ, 1995) and the Richardson plot (Russ, 1995). One of the most widely used algorithm is the box counting algorithm which is based on the positioning of a scale shifting grid over the object’s binary representation (white pixels on a black pixels background) and subsequent pixels value examination for each box. Accordingly to their values, each box will be placed in one of the following categories, as shown in Figure 2.28:

Interior box → When it is fully positioned within the body of the object (box containing white pixels only and no adjacent black pixel).

Border box → When it crosses the boundary of the object (box containing at least one white pixel and one or more adjacent black pixels).

Empty box → Boxes containing only black pixels.

Figure 2.28 – Border box, interior box and empty box (larger squares).

In pixel representation the area of any object can be referred as the sum (or number) of J sized boxes NJ. Furthermore, it is known that the total box area needed to

Border Box

Interior Box

Empty Box



fully enclose a fractal surface is linearly dependent on J-D, D being the fractal dimension. Therefore, for a fractal object NJ must be proportional to J-D:

DJN KJ −= (2.49)

Applying logarithms to both sides of the equation:

( ) ( ) ( )JLog N Log K DLog J= − (2.50)

Representing –Log(NJ) as a function of Log(J) one obtains a straight line with a D slope, which can be determined by linear regression of the data points, as shown in Figure 2.29.

Figure 2.29 – Graphical representation of a fractal dimension.

A fractal dimension is called structural when it reports to parameters such as the object surface or its shape overall. These fractal dimensions are comprehended between 1 and 2 with smaller values for low space-filling or less compact objects (value of 1 for lines) and higher values for better space-filling and homogeneous forms (value of 2 for circles). A fractal dimension is called textural when it reports to parameters such as the object perimeter or its contour. Like the structural fractal dimensions they are comprehended between 1 and 2 but, with smaller values for objects with a regular contour and higher values for highly irregular objects (Obert et al., 1990).

The fractal dimensions determined in this work were as follows:

Mass Fractal Dimension (DBM)

The Mass Fractal Dimension DBM is calculated upon determining the sum of J sized boxes NJ (Area), for each J sized box grid, and is given by the slope of Log(NJ) vs. Log(J) (Obert et al., 1990).

Surface Fractal Dimension (DBS)

The Surface Fractal Dimension DBS is calculated upon determining the sum of J sized boxes crossing the object border PJ (Perimeter), for each J sized box grid, and is given by the slope of Log(PJ) vs. Log(J) (Obert et al., 1990).

D

Log (J)

-Log

(N J)



Area vs. Perimeter Fractal Dimension ( APD )

The Area vs. Perimeter Fractal Dimension APD relates the Areas with the Perimeters of

the whole set of objects and is given by the slope of Log(A) vs. Log(P) (Soddell and Seviour, 1994).

Area vs. Feret diameter Fractal Dimension ( AFDD )

The Area vs. Feret diameter Fractal Dimension AFDD relates the Areas with the Feret

Diameters of the whole set of objects and is given by the slope of Log(A) vs. Log(FMax) (Soddell and Seviour, 1994).

Perimeter vs. Feret diameter Fractal Dimension ( PFDD )

The Perimeter vs. Feret diameter Fractal Dimension PFDD relates the Perimeters with the

Feret Diameters of the whole set of objects and is given by the slope of Log(P) vs. Log(FMax) (Soddell and Seviour, 1994).

Mass ratio Fractal Dimension (DMR)

The Mass ratio Fractal Dimension, which determines the relationship between the mass of an object enclosed within different radii (r), is calculated upon determining the sum of L sized boxes Ar (Area), for each r sized circle, and is given by the slope of Log(Ar) vs. Log(r) (Soddell and Seviour, 1994).

Euclidean Distance Map Fractal Dimension (DEDM)

The Euclidean Distance Map Fractal Dimension is given by (Pons and Vivier, 1999):

1EDM fD D= − (2.51)

where Df is the slope of Log(Pλ) vs. Log(λ) and the Perimeter Pλ for each distance (λ) is given by:

1( )

kEDM k

P

λ

λ λ==∑

(2.52)

Materials and Methods Multivariable Statistical Techniques 2.5


2.5 MULTIVARIABLE STATISTICAL TECHNIQUES

In the protozoa(n) and metazoa(n) identification work, the obtained morphological parameters were subsequently treated in order to allow the classification of each species. Hence, multivariable statistical techniques such as Discriminant Analysis (DA) and Neural Networks (NN) were employed. In the activated sludge study the Partial Least Squares (PLS), technique was employed to determine the relationships between the aggregates morphological parameters and operating parameters. The Matlab statistical analysis toolbox was employed for all of the above mentioned techniques.

It is not the intention of this section to thoroughly describe these techniques and thus only a brief report is provided. For a more comprehensive description of these techniques some references are provided.

Materials and Methods 2.5 Multivariable Statistical Techniques


2.5.1 PARTIAL LEAST SQUARES

Partial Least Squares (PLS) regression can be considered as a common method of least squares regression. In this method, the latent variables ui (matrix U) are used for modelling the objects separately in the matrix of Y dependent data, whereas, the ti variables (T) are used for modelling the objects separately in the matrix of X independent data. The latent variables U and T are the basis of the regression model and are determined in an iterative process with the centred matrices of X and Y as starting points, as shown in the following equation (Einax et al., 1997):

11 12 1

21 22 1

1 2

my

my

n n nmy

y y yy y y

Y

y y y

U

=

11 12 1

21 22 1

1 2

mx

mx

n n nmx

x x xx x x

X

x x x

T

=

(2.53)

U A T E= +i (2.54)

Furthermore, the latent variables hold the following properties: Regression errors between U (ui) and T (ti) have a minimum. ui are orthogonal linear combinations of the features in Y modelling the objects

in Y, whereas, ti are orthogonal linear combinations of the features in X modelling the objects in X.

A maximum correlation between ui and ti occurs when i=j. The pairs ui and ti (i=1,…,n) explain the covariance between X and Y in

descending order.

Partial Least Squares (PLS), in a similar way as factor analysis and principal components analysis, extracts linear combinations of the essential features modelling the original X and Y data. However, PLS also models the dependence of the two data sets being well suited for multivariate calibration. The most important advantage of this method reports to the nonproblematic handling of multicollinearities relying on an iterative algorithm which makes possible the treatment of data with more features than objects.

The effectiveness of the PLS model fit is determined by the error of the prediction in a manner similar to the ordinary least squares methods. The error of the prediction, as well as the number of U and T significant vectors can be determined by the cross-validation test. This procedure is accomplished by deleting a certain number of values in the Y matrix, determining the PLS model which will predict the omitted values and finally comparing them with the original values.



ˆ... ...ˆ... ...

´

ˆ... ...

y y y y yy y y y y

Y

y y y y y

• • = →

•

(2.55)

The omitted values are represented by • and the predicted values by y .

The above mentioned procedure is repeated until all values have been omitted once, so an error of prediction can be determined. This predicted residual error sum of squares (PRESS) is the parameter that limits the number of the u and t latent vectors:

2

1 1( )

myn

n iji j

PRESS t E= =

=∑∑ (2.56)

and:

1

( ) 1( )

k

k

PRESS tPRESS t +

> (2.57)

If an additional latent vector does not improve the error of prediction therefore, having no adequate effect on the model, noise cannot be predicted and a minimum value of PRESS will be reached.

PLS modelling is very well suited for modelling relationships where one can simulate changes in X and observe the corresponding changes in Y. Furthermore, it is possible to interpret the t and u latent vectors since the latent vectors scores for each object can be used to display the objects or, alternatively, compute the correlation between the original features and the latent vectors to assess the kind of interacting features for both data sets.



2.5.2 DISCRIMINANT ANALYSIS

Discriminant Analysis (DA), in a similar way as factor analysis and principal components analysis, defines new variables (discriminant functions) as linear combinations of the input parameters. This technique increases the variability between classes instead of the variability within classes of principal components analysis obtaining thus, an increased separability between the different classes studied. Thus, this technique allows the modelling of the different classes in order to reclassify them with the least error or classify new test samples (Einax et al., 1997).

The mathematical procedure for the calculation of the discriminant functions resides on the resolution of the eigenvalues γ for the 1B W −i quotient, i. e., to determine the roots and the eigenvectors e (Einax et al., 1997):

1( ) i i iB W e eγ− =i i i (2.58)

where:

( )( )1

TotalTk

k kkk

B n x x x x=

= − −∑ (2.59)

( )( )1 1

Total kTk n

k kk kn knk n

W n x x x x= =

= − −∑∑ (2.60)

The non-trivial solution leads to: 1 0B W Iγ− − =i (2.61)

This solution originates pairs of eigenvalues and eigenvectors where the latest are orthogonal and the eigenvalues express the extracted variance of the 1B W −i matrix. The first eigenvalue equals the extracted variance with the first eigenvector, the second eigenvalue equals the extracted variance with the second eigenvector and continuously from then on, in which the sum of all the eigenvalues equals the total variance in 1B W −i .

The coefficients of the discriminant functions are the unstandardized coefficients of e. As the direct interpretation of these unstandardized coefficients may lead to erroneous conclusions new standardized coefficients are determined which are useful on the determination of the nature of the classes’ differences:

T

eae e

= (2.62)

The coordinates of the objects on the new discriminant functions space are determined with the original parameters of each object. Assuming ndf discriminant functions the new ndf values of each object are given by:

var var1 1 2 2 var var... ...df df df df dfi i i i n n ival e x e x e x e x= + + + + + (2.63)

where dfival is the new value of the object for the df discriminant function of the

object i, vardfe is the coefficient of the original var variable of the df discriminant function

and xvari is the value of the original var variable of the object i.



The classification of new objects in a class is determined by the class in which region it falls. This may be determined through:

( )2

21

dfnk

k ji jj

F C df df=

= −∑ (2.64)

where kjdf is the average value of the discriminant function j in class k, Fk is the

classification value of each object in class k and, C2 is defined as:

( )2

11

obj Total df k

kdf obj Total

n k n nCnn n k

− − +=

+− (2.65)

where Ktotal is the total number of classes, nobj the total number of objects and, nk the number of objects of class k.

Each class has attributed to it an ndf-dimensional region. If the value of Fk of an object is less than or equal to 1 2( , , )F f f q then the object belongs to the k class, whereas in the case of overlapping regions, the class with the less Fk value is favoured. The F values are obtained by the Fisher F table (Einax et al., 1997), where:

1 dff n= (2.66)

2 1obj Total dff n k n= − − + (2.67)

for a probability of:

1q α= − (2.68)

As in factor analysis the new discriminant functions space may have an ndf-dimension smaller than the number of input parameters. Regarding the reclassification in classes the number of necessary discriminant functions is (Einax et al., 1997):

( )varmin 1,df Totaln k n= − (2.69)

However, when all the input parameters are needed to determine the new discriminant functions Discriminant Analysis does not provide an effective parameter reduction.



2.5.3 NEURAL NETWORKS

An artificial Neural Network (NN) is a biologically inspired computational model consisting on processing elements (neurons) and connections between them with associated coefficients (weights). This assembly, which is called the neuronal structure, is then trained with the help of recall algorithms (Kasabov, 1996).

In general, the model of an artificial neuron (Figure 2.30) is based on the following parameters (Kasabov, 1996, Demuth and Beale, 2001):

Input connections (x1, x2, …, xn) → These inputs have weights (w1, w2, …, wn) bound to them and one unitary input (bias) linked to each neuron and an associated weight.

Input function (f) → Determines the aggregated net input signal to the neuron as:

( , )u f x w= (2.70)

The function f is usually the summation function:

0

n

i ii

u x w=

=∑ (2.71)

Activation function (fa) → Determines the activation level of the neuron and is normally the hard-limit, linear or sigmoid, as shown in Figure 2.31.

Output function (fo) → Determines the output signal value emitted through the output of the neuron:

( )o af f u= (2.72)

A model of a three input artificial neuron is provided in Figure 2.30.

Figure 2.30 – A model of an artificial neuron.

The most used activation functions are shown in Figure 2.31.

n

i iu x w=∑∑ fa

x1

x2

…

…

xn

w1

w2

wn

x0 = 1

w0

fo



Figure 2.31 – Main activated functions: a) hard-limit threshold b) saturated linear threshold (b1-positive range; b2- total range) c) sigmoid (c1- logistic; c2- bipolar logistic; c3- Gaussian).

Several different type of neurons are usually used, differing on the above mentioned parameters and giving output values ranging from binary {0,1}, bivalent {-1,1}, continuous [0,1] or discrete numbers in a defined interval.

Although a single neuron can perform certain simple information-processing functions, the power of Neural Networks comes from connecting neurons in networks. An artificial Neural Network is a computational model defined by the following parameters (Kasabov, 1996):

Type of neurons (nodes). Connectionist architecture which is the organization of the connections

between models. Learning algorithm. Recall algorithm.

A simple Neural Network is shown in Figure 2.32. It contains four input nodes, two intermediate, and an output node.

O

U

O

U

O

U

O

U O

U

O

U

(a) (b1)

(b2)

(c1)

(c2) (c3)



Figure 2.32 – Simple neural network.

The most attractive characteristic of Neural Networks is their ability to learn, which makes possible the modification of the behaviour in response to the environment. A Neural Network is trained with a training set X of input vectors to produce the desired set of output vectors Y, while learning the internal structure of the data. The training process is achieved by changing the connection weights of the network so that these gradually converge to the optimal values. Learning occurs if after supplying a training example, a change in at least one synaptic weight takes place.

The learning ability of a Neural Network is achieved by a learning (training) algorithm, which is usually classified as (Kasabov, 1996):

Supervised → Training examples include both the input vectors and the output vectors. The algorithm approximates a desired function.

Unsupervised → Only the input vectors are available, so the algorithm only learns some internal features of the input vectors.

Reinforcement learning → Is a combination of the above-mentioned algorithms based on presenting the input vectors and looking at the resulting output vector. If the result is considered as satisfactory the existing connection weights are increased, otherwise they are decreased.

The neurons used in the perceptron (see Figure 2.33) have a simple summation input function and a hard-limited or a linear threshold activation function, while the input values are generally real numbers and the output is binary. The connection structure of a perceptron is feed forward and three-layered. The first layer (buffer) represents the inputs, the second (feature layer) represents new features and the third (perceptron layer) the outputs. The weights between the buffer and the feature layer are fixed resulting in a two-layer representation. Being a supervised learning algorithm, the perceptrons only learns when it misclassifies an input vector, leading to a change in the weights of the function. Perceptrons are usually used to solve problems with linearly separable classes given that they are excellent linear discriminators.

n7 n5

n6

n1

n3

n4

n2 fo(n7)

Input Layer Hidden layer Output layer



Figure 2.33 – A single two-input, one-output perceptron with a bias.

To overcome the linear separability limitation of the perceptrons, Multilayer Perceptrons (MLP) were introduced. An MLP consists of an input layer, at least one intermediate or ‘hidden’ layer, and an output layer where the neurons of each layer are fully connected to the ones in the next layer. The neurons in the MLP have continuous value inputs and outputs, a summation input function, and a nonlinear activation function. The interactions between the input variables can be learned in multilayer perceptrons, showing the degree of interaction between the variables and their importance to approximating the goal function.

The emergence of the multilayer perceptrons required the development of new learning algorithms such as the error back propagation algorithm. In this case, the training algorithm consists of two steps: a forward pass, when inputs are applied and propagated through the intermediate layers to the output layer; and a backward pass, when an error is calculated at the outputs and propagated backwards for determining the weight’s changes.

A gradient descent rule is used, for finding the optimal connection weights (whg) of all the neurons g at each node h, that minimize the error E. The weight change ∆whg, at a t+1 iteration is given by (Kasabov, 1996):

( )( 1) / ( )hg hgw t lrate E w tδ δ∆ + = − (2.73)

where lrate is the learning rule.

The gradient rule guarantees that after a number of iterations the error E will reach a minimum and, is given by:

pg

p gE Err=∑∑ (2.74)

where the error pgErr for an example p can be determined as:

( )2

2

p pg ogp

g

y fErr

−= (2.75)

where pgy is the desired output value and p

ogf the obtained output value.

w0

w1 w2

y

x0

x1 x2



However, several problems may arise when using the back propagation algorithm (Kasabov, 1996):

An excessively high learning rate causes large amplitude oscillation of the weights.

A small learning rate causes a very slow convergence. The algorithm can stop at a local minimum, instead of the global minimum. The algorithm is very time consuming. Over fitting problems. Finding the number of optimal hidden layers and hidden nodes.

145

3 RESULTS AND DISCUSSION

146

3.1 Aerobic Wastewater Treatment Processes 147 3.1.1 Protozoa and metazoa identification 148 3.1.1.1 Discriminant Analysis 148 3.1.1.2 Neural Networks 153 3.1.2 Activated sludge monitoring 158 3.1.2.1 Operating Parameters Monitoring 158 3.1.2.2 Morphological Parameters Monitoring 159 3.1.2.3 PLS Analysis 168 3.2 Anaerobic Wastewater Treatment Processes 175 3.2.1 Anaerobic granulation process monitoring 176 3.2.1.1 Operating Parameters Monitoring 176 3.2.1.2 Dilution Study 178 3.2.1.3 Morphological Parameters Monitoring 180 3.2.2 Granule deterioration triggered by oleic acid 193 3.2.2.1 Operating Parameters Monitoring 193 3.2.2.2 Morphological Parameters Monitoring 195 3.2.2.3 Fines Fraction Monitoring 206

Results and Discussion Aerobic Wastewater Treatment Processes 3.1



Regarding the protozoa and metazoa identification, the organism’s global recognition percentage achieved a value of 85.1% for the Discriminant Analysis and 81% for the Neural Network. About 1.5% of the organisms were not recognized by this procedure and 13.3% were misclassified for the Discriminant Analysis whereas for the Neural Network these values were 3% and 16% respectively. Regarding the identification of the main protozoa and metazoa groups as well as the ciliated protozoa groups, the values of the micro-organism’s global recognition percentage was over 97%, the misclassification bellow 1.5% and about 1.5% were not identified for the Discriminant Analysis whereas for the Neural Network those values were respectively over 95%, around 2% and 2.9%. With respect to the assessment of plant conditions the global recognition percentages were over 89% and the misclassification errors bellow 9.1% for the Discriminant Analysis whereas for the Neural Network those values were respectively above 86% and bellow 11%. Comparing the two multivariable statistical techniques, the overall results were lower for the Neural Networks than for the Discriminant Analysis with the exception of the critical conditions assessment.

In the activated sludge monitoring experiment the very high SVI values, ranging from 200 mL/g to 620 mL/g, denoted the existence of a severe bulking problem. Analysing the Aggregates Equivalent Diameter it was found an overall decreasing trend throughout the survey time for all the size classes. The aggregates larger than 1 mm diameter proved to be not significant with Area Percentages of less than 5% for all times whereas the 0.1-1 mm class was predominant with values ranging from 60% to 95%, and the 0.02-0.1 mm class attained values between 5% and 40%. With respect to the aggregates morphological analysis the two classes of larger aggregates were found to be somewhat loose and elongated structures presenting rough edges. From the analysis of the TL/TSS ratio their values ranged from 10000 to 60000 m/g clearly indicating the existence of a filamentous bulking problem. The PLS analysis did not provide a clear relationship between the SVI and the morphological parameters, however a correlation factor of 0.8854 was obtained for the TL/TSS ratio, with only 3 rejected values. With respect to the TSS, the PLS analysis revealed a clear relationship with the Total Aggregates Area with a satisfactory correlation factor of 0.9335 for 2 rejected values.

Results and Discussion 3.1 Aerobic Wastewater Treatment Processes


3.1.1 PROTOZOA AND METAZOA IDENTIFICATION

The main results of the protozoa and metazoa identification are further presented for both the Discriminant Analysis and the Neural Networks techniques. For both techniques a number of classification procedures were performed namely the overall protozoa and metazoa recognition, main protozoa and metazoa groups, ciliated protozoa groups, effluent quality, aeration, sludge age, and nitrification.

3.1.1.1 DISCRIMINANT ANALYSIS

For the Discriminant Analysis technique the studied micro-organisms were first separated into two easily recognizable classes: stalked and non-stalked micro-organisms. This step was performed by the user in order to simplify and speed up the image analysis programme and due to the fact that represents a quite simple characteristic for the user to establish. Subsequently, Discriminant Analysis was performed for each possible combination between the micro-organisms for all the groups, in a total of 28 (7+6+…+2+1) for the stalked group and 78 (12+11+…+2+1) for the non-stalked group.

Initially, 75 individuals (training set) of each of the 21 micro-organisms were used for the determination of the discriminant functions, with exception of Trochilia with 37, Aelosoma sp. with 66, Digononta with 67 and Nematoda and Suctoria with 50). For validation purposes a different set of 25 individuals (test set) of each of the 21 micro-organisms was used, with exception of the above mentioned in which the same organisms of the identification process were used, due to sample number limitations.

In the validation process in order to determine each micro-organism group all the combinations between each group pair were surveyed. First, the difference between the location of the micro-organism in the new variables space and each group average location was determined and, seemingly the micro-organism was attributed to its closest group. After this procedure was applied for all of the possible combinations, the micro-organism was attributed to the group with more matches, that is, to the group where it was found to be attributed the most.

The values obtained for the overall micro-organisms identification are shown in Table 3.1. In this work aci represents Aspidisca cicada, ael represents Aelosoma sp., arc represents Arcella sp., car represents Carchesium sp., epi represents Epistylis sp., eug represents Euglypha sp., eup represents Euplotes sp., dig represents Digononta order, lit represents Litonotus sp., mon represents Monogononta order, nem represents Nematoda sub-class, ope represents Opercularia sp., per represents Peranema sp., suc represents Suctoria sub-class, tra represents Trachelophyllum sp., tri represents Trithigmostoma sp., tro represents Trochilia sp., vaq represents Vorticella aquadulcis, vmi represents Vorticella microstoma, vor represents other Vorticella species (mainly Vorticella convalaria), zoo represents Zoothamnium sp. and NI represents non identified micro-organisms.

The organism’s global Recognition Percentage achieved a value of 85.1%, the misclassification error 13.3% and 1.5% were not identified. Note that the misclassification error, on the following tables, of each micro-organism refers to the percentage of all the other micro-organisms incorrectly classified as that micro-organism. These values can be considered as quite reasonable especially in terms of the protozoa and metazoa



recognition whereas for the micro-organisms misclassification the results were not as positive. However, in a global analysis it can be considered that a satisfactory overall recognition level was attained.

Table 3.1 – Micro-organisms recognition (number and percentage).

aci ael arc car dig epi eug eup lit mon nem ope per suc tra tri tro vaq vmi vor zoo NI aci 19 0 0 0 0 0 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 ael 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 arc 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 car 0 0 0 16 0 3 0 0 0 0 0 1 0 0 0 0 0 0 0 1 4 0 dig 0 0 0 0 24 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 epi 0 0 0 0 0 21 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 eug 0 0 0 0 0 0 24 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 eup 1 0 0 0 0 0 0 23 1 0 0 0 0 0 0 0 0 0 0 0 0 0 lito 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 2

mon 0 0 0 0 3 0 0 0 1 20 0 0 0 0 0 0 0 0 0 0 0 1 nem 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0 0 0 ope 0 0 0 1 0 6 0 0 0 0 0 16 0 0 0 0 0 0 1 0 1 0 per 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0 suc 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 tra 1 0 0 0 0 0 0 0 0 0 0 0 1 0 22 0 0 0 0 0 0 1 tri 3 0 1 0 0 0 0 0 0 1 0 0 0 0 0 18 0 0 0 0 0 2 tro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 vaq 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 20 4 0 0 0 vmi 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 6 14 0 1 0 vor 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 1 0 zoo 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 15 0 Rec.

% 76 100 100 64 96 84 96 92 92 80 100 64 100 100 88 72 100 80 56 88 60 76

Err. % 21 0 4 38 11 30 4 12 8 9 0 38 4 0 0 5 0 23 26 15 32 21

The micro-organisms identification level is shown in Table 3.2. An excellent recognition level refers to recognition percentages above 95%, a good recognition level to percentages between 90% and 95%, a reasonable recognition level to percentages between 75% and 90% and, a poor recognition level to percentages bellow 75%. For the misclassification levels, an excellent misclassification level refers to misclassification percentages bellow 5%, a good level to percentages between 5% and 10%, a reasonable level to percentages between 10% and 25%, and a poor level to percentages above 25%.

Analysing the 21 studied micro-organisms, 16 shown reasonable to excellent recognition levels, whereas 5 shown a poor recognition level. Furthermore, 16 micro-organisms presented reasonable to excellent misclassification levels meaning that 5 presented poor misclassification levels which can not be considered as completely positive but do not compromise this work. Moreover, an in-depth analysis shows that the micro-organisms with poorer results were mainly the stalked protozoa whereas the vast



majority of the non-stalked protozoa and metazoa attained reasonable to excellent recognition and misclassification performances.

Table 3.2 – Recognition and misclassification levels. Recognition Misclassification Poor Reasonable Good Excellent Poor Reasonable Good Excellent

aci ● ● ael ● ● arc ● ● car ● ● dig ● ● epi ● ● eug ● ● eup ● ● lito ● ●

mon ● ● nem ● ● ope ● ● per ● ● suc ● ● tra ● ● tri ● ● tro ● ● vaq ● ● vmi ● ● vor ● ● zoo ● ●

Total 5 6 2 8 5 5 3 8

The values obtained for the identification of the main protozoa and metazoa groups are shown in Table 3.3 and for the ciliated protozoa in Table 3.4. The micro-organism’s global Recognition Percentage of the main protozoa and metazoa groups achieved a value of 97.3%, the misclassification 1.1% and 1.5% were not identified, whereas for the ciliated protozoa groups the organism’s global Recognition Percentage achieved a value of 97.0%, the misclassification error 1.5% and 1.5% were not identified.

Table 3.3 – Main protozoa and metazoa group’s recognition (number and percentage). Flagellates Ciliates Sarcodines Metazoa NI

Flagellates 25 0 0 0 0 Ciliates 1 339 2 1 7

Sarcodines 0 1 49 0 0 Metazoa 0 1 0 98 1 Rec. % 100.0 96.9 98.0 98.0 Error % 0.7 1.6 1.5 0.8 1.5



Table 3.4 – Main ciliates group’s recognition (number and percentage).

Carnivorous Crawling Free swimming Sessile Non

Ciliates NI

Carnivorous 48 0 0 0 0 2 Crawling 1 92 0 0 3 4

Free swimming 0 1 22 0 1 1 Sessile 0 0 0 175 0 0

Non Ciliates 1 1 0 0 172 1 Rec. % 96.0 92.0 88.0 100.0 98.3 Error % 1.9 2.0 0.0 0.0 3.8 1.5

With respect to the identification of both the main protozoa and metazoa groups (flagellate protozoa, ciliate protozoa, sarcodine protozoa and metazoa) and the ciliated protozoa groups (carnivorous, crawling, free-swimming and sessile), the results can be considered as quite good. For both cases the values of the micro-organism’s global Recognition Percentage was very high and no significant misclassification problems were registered.

The values obtained for the identification of the sets of micro-organisms indicating the plants operating conditions, such as the effluent quality, aeration, sludge age and presence of nitrification are shown in Table 3.5, Table 3.6, Table 3.7 and Table 3.8. In order to allow the assessment of the wastewater treatment plant operating conditions, the representative micro-organisms of each condition must be specified. Therefore, and accordingly to Madoni (1994a), Jahn et al. (1999) and Canler et al. (1999), the correspondence between the micro-organisms and operating conditions is as follows.

Opercularia sp., Trachelophyllum sp. and V. microstoma for low effluent quality; Aelosoma sp., Arcella sp., Carchesium sp., Epistilys sp., Euglypha sp., Euplotes sp., Monogononta order, Peranema sp., Trithigmostoma sp., Trochilia sp., V. aquadulcis and Zoothamnium sp. for high effluent quality.

Nematoda sub-class, Opercularia sp. and V. microstoma for low aeration (bellow 0.2-0.5 mg O2/L); Aelosoma sp., Arcella sp., Carchesium sp., Euglypha sp., Monogononta order, Trochilia sp., V. aquadulcis and Zoothamnium sp. for good aeration (above 1-2 mg O2/L).

Peranema sp. and V. microstoma for fresh sludge (less than a few day according to Canler et al. (1999)); Aelosoma sp., Arcella sp., Digononta order, Euglypha sp. and Monogononta order for old sludge (20 days or more according to Canler et al. (1999)).

Aelosoma sp., Arcella sp., Carchesium sp., Epistilys sp., Euplotes sp., Monogononta order and Trochilia sp. for the presence of nitrification.

The organism’s global Recognition Percentage for the effluent quality characterization achieved a value of 89.3%, the misclassification error 9.1%, and 1.5% were not identified, for the aeration assessment were respectively 90.1%, 8.4% and 1.5%, for the sludge age characterization were 94.3%, 4.2%, and 1.5%, and for the nitrification presence characterization the values were 91.2%, 7.2%, and 1.5%.



Table 3.5 – Effluent quality assessment (number and percentage). Effluent quality

High Medium or

Indiscriminate Low NI

High 274 13 10 3 Medium or

Indiscriminate 8 138 0 1

Low 16 1 57 4 Rec. % 91.3 92.0 76.0 Error % 13.8 7.9 6.4 1.5

Table 3.6 – Aeration assessment (number and percentage). Aeration

High Medium or




Low 9 6 60 7 Rec. % 90.5 92.8 80.0 Error % 9.3 10.3 6.1 1.5

Table 3.7 – Sludge age assessment (number and percentage). Sludge age

Fresh Medium or

Indiscriminate Old NI

Fresh 39 11 0 0 Medium or


Old 0 2 122 1 Rec. % 78.0 95.4 97.6 Error % 3.8 7.5 1.7 1.5

Table 3.8 – Nitrification assessment (number and percentage). Nitrification

Presence No Indication NI Presence 158 16 1

No Indication 22 321 7 Rec. % 90.3 91.7 Error % 8.1 6.3 1.5



In relation to the plant conditions assessment the overall results proved to be fairly good both in terms of the operating conditions assessment and the misclassification levels. However, the assessment of critical conditions such as low effluent quality, low aeration and fresh sludge, proved to be much poorer with recognition and misclassification levels barely reasonable.

3.1.1.2 NEURAL NETWORKS

For the Neural Network technique the studied micro-organisms were first separated into two easily recognizable classes (stalked and non-stalked micro-organisms) as for the Discriminant Analysis. Subsequently, Neural Networks were performed for each possible combination between the micro-organisms for all the groups. The training and the test sets used in this technique were the same as for the previous technique, with 75 individuals for the training set and 25 different individuals for the test set.

The programmed Neural Network was a feed forward Neural Network (back propagation algorithm) with logistic sigmoidal activation functions consisting of a 2 layer network with no hidden layers and 8 input and output nodes for the stalked micro-organisms and 13 input and output nodes for the non-stalked. The training and learning functions were the gradient descent algorithms and the error is given by the mean square error. Although some attempts were made with more complex Neural Networks, namely with 3 layers with respectively and 3 and 8 nodes in the hidden layer for the stalked micro-organisms and 4 and 13 nodes hidden layer for the non-stalked, their results did not significantly surpassed the obtained for the simpler network. Moreover, the computing time was largely unfavourable for these higher complex Neural Networks and therefore, the simplest Neural Network was chosen and its results are described in this section.

In the validation process in order to determine each micro-organism group all the combinations between each group pair were surveyed. The applied Neural Networks aimed at obtaining an output value of 1 for the micro-organism correct group and 0 for the incorrect group. Therefore, for each group pair the micro-organism was attributed to the group with the higher output value larger than 0.01. After this procedure was applied for all of the possible combinations, the micro-organism was attributed to the group with more matches, that is, to the group where it was found to be attributed the most.

The values obtained for the micro-organisms identification are shown in Table 3.9. The organism’s global Recognition Percentage achieved a value of 81%, the misclassification error 16% and 3% were not identified. These values can be considered as fairly reasonable in terms of the protozoa and metazoa recognition whereas for the micro-organisms misclassification the results were not as positive leading to a barely satisfactory overall recognition level. Furthermore, it should be noticed that these results remained lower than the ones obtained by the Discriminant Analysis technique.



Table 3.9 – Micro-organisms recognition (number and percentage).

aci ael arc car dig epi eug eup lit mon nem ope per suc tra tri tro vaq vmi vor zoo NI aci 17 1 0 0 0 0 1 5 0 0 0 0 0 0 0 1 0 0 0 0 0 0 ael 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 arc 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 car 0 0 0 17 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 2 3 0 dig 0 0 0 0 23 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 epi 0 0 0 1 0 21 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 eug 0 0 0 0 0 0 24 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 eup 1 0 0 0 0 0 1 22 1 0 0 0 0 0 0 0 0 0 0 0 0 0 lito 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 2

mon 0 0 0 0 3 0 0 0 1 16 0 0 0 0 0 0 0 0 0 0 0 5 nem 0 0 1 0 0 0 0 0 0 0 24 0 0 0 0 0 0 0 0 0 0 0 ope 0 0 0 1 0 5 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 per 0 0 0 0 0 0 0 0 1 0 0 0 24 0 0 0 0 0 0 0 0 0 suc 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 tra 2 0 0 0 0 0 0 1 0 0 0 0 0 0 21 0 0 0 0 0 0 1 tri 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 5 tro 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 0 vaq 0 0 0 2 0 1 0 0 0 0 0 2 0 0 0 0 0 14 5 0 1 0 vmi 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 2 15 0 1 1 vor 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 2 0 zoo 0 0 0 7 0 2 0 0 0 0 0 1 0 0 0 0 0 0 1 3 11 0 Rec.

% 68 100 100 68 92 84 96 88 92 64 96 76 96 100 84 64 96 56 60 80 44

Err. % 29 4 7 45 12 28 8 21 12 6 0 42 0 0 0 11 0 13 29 23 39 3

The micro-organisms identification level is shown in Table 3.10. Analysing the 21 studied micro-organisms, 14 shown reasonable to excellent recognition levels, whereas 7 shown a poor recognition level. Furthermore, 15 micro-organisms presented reasonable to excellent misclassification levels meaning that 6 presented poor misclassification levels which, once again, neither can be considered as completely positive nor compromise this work. The in-depth analysis illustrates, once more, the stalked protozoa with poorer results with respect to the vast majority of the non-stalked protozoa and metazoa in both recognition and misclassification performances. Again these results were found to be inferior to the ones obtained by the Discriminant Analysis technique.



Table 3.10 – Recognition and misclassification levels. Recognition Misclassification Poor Reasonable Good Excellent Poor Reasonable Good Excellent

aci ● ● ael ● ● arc ● ● car ● ● dig ● ● epi ● ● eug ● ● eup ● ● lito ● ●

mon ● ● nem ● ● ope ● ● per ● ● suc ● ● tra ● ● tri ● ● tro ● ● vaq ● ● vmi ● ● vor ● ● zoo ● ●

Total 7 5 2 7 6 6 3 6

The values obtained for the identification of the main protozoa and metazoa groups are shown in Table 3.11 and for the ciliated protozoa in Table 3.12. The micro-organism’s global Recognition Percentage of the main protozoa and metazoa groups achieved a value of 95.6%, the misclassification 1.5% and 2.9% were not identified, whereas for the ciliated protozoa groups the organism’s global Recognition Percentage achieved a value of 95.0%, the misclassification error 2.1% and 2.9% were not identified.

For both the main protozoa and metazoa groups and the ciliated protozoa groups, the results can be considered as quite good with high global Recognition Percentages and no significant misclassification, though poorer than the ones of the Discriminant Analysis.

Table 3.11 – Main protozoa and metazoa group’s recognition (number and percentage). Flagellates Ciliates Sarcodines Metazoa NI

Flagellates 24 1 0 0 0 Ciliates 0 337 3 1 9

Sarcodines 0 1 49 0 0 Metazoa 0 1 1 92 6 Rec. % 96.0 96.3 98.0 92.0 Error % 0.0 2.4 2.9 0.8 2.9



Table 3.12 – Main ciliates group’s recognition (number and percentage).

Carnivorous Crawling Free swimming Sessile Non

Ciliates NI

Carnivorous 48 0 0 0 0 2 Crawling 1 90 0 0 4 5

Free swimming 0 3 21 0 0 1 Sessile 0 0 0 174 0 1

Non Ciliates 2 1 0 0 166 6 Rec. % 96.0 90.0 84.0 99.4 94.9 Error % 2.8 4.1 0.0 0.0 3.9 2.9

The values obtained for the identification of the sets of micro-organisms indicating the plants operating conditions, such as the effluent quality, aeration, sludge age and presence of nitrification are shown in Table 3.13, Table 3.14, Table 3.15 and Table 3.16. The organism’s global Recognition Percentage for the effluent quality characterization achieved a value of 86.1%, the misclassification error 11.0%, and 2.9% were not identified, for the aeration assessment were respectively 87.8%, 9.3% and 2.9%, for the sludge age characterization were 92.8%, 4.4%, and 2.9%, and for the nitrification assessment the values were 87.6%, 9.5%, and 2.9%.

Table 3.13 – Effluent quality assessment (number and percentage). Effluent quality

High Medium or




Low 15 2 61 3 Rec. % 86.3 88.0 81.3 Error % 15.1 10.8 8.0 2.9

Table 3.14 – Aeration assessment (number and percentage). Aeration

High Medium or




Low 11 5 64 9 Rec. % 84.5 91.2 85.3 Error % 9.9 11.0 7.7 2.9



Table 3.15 – Sludge age assessment (number and percentage). Sludge age

Fresh Medium or

Indiscriminate Old NI

Fresh 39 10 0 1 Medium or


Old 0 2 117 6 Rec. % 78.0 94.6 93.6 Error % 3.8 7.0 2.9 2.9

Table 3.16 – Nitrification assessment (number and percentage). Nitrification

Presence No Indication NI Presence 151 19 5

No Indication 31 309 10 Rec. % 86.3 88.3 Error % 11.4 7.7 2.9

In relation to the plant conditions assessment the overall results proved to be fairly good both in terms of the operating conditions assessment and the misclassification levels. However, the assessment of critical conditions such as low effluent quality, low aeration and fresh sludge, proved to be somewhat poorer with recognition and misclassification levels just reasonable. However, and contrarily to the overall results, the assessment of the critical conditions proved to be more satisfactory with the Neural Networks than for the Discriminant Analysis technique.



3.1.2 ACTIVATED SLUDGE MONITORING

In this work the activated sludge of AGERE, E.M. in Frossos (Braga) wastewater treatment plant was surveyed by means of image analysis of both the aggregates contents and morphology and the free filamentous bacteria contents. With this purpose the Filaments Length and contents as well as the aggregates contents and 7 different morphological parameters were determined (see Section 2.1.1). The results presented here refer only to the most significant on this survey and therefore, only 3 (Solidity, Eccentricity and Convexity) of the 7 morphological parameters are shown. Also, two parameters originating from the filaments contents and the aggregates contents (TL/TA) in one hand and from the solids contents (TL/TSS) on the other were included due to its importance on the prediction of the Sludge Volume Index. Overall, approximately 140000 aggregates were studied in this survey. Upon the determination of the trend lines some points had to be discarded and therefore, they appear as grey points instead of black in each figure.

3.1.2.1 OPERATING PARAMETERS MONITORING

The evolution of the Sludge Volume Index and the Total Suspended Solids were determined by Frossos wastewater treatment plant laboratory (Rodrigues, 2000) and are shown in Figure 3.1. Analysing the Sludge Volume Index it is notorious that throughout all the survey time the values were very high ranging from 200 mL/g to 620 mL/g, implying thus the existence of a severe bulking problem. Furthermore, this situation was more problematic in the period between day 67 and 87 with values normally higher than 500 mL/g. The Total Suspended Solids contents were not far from the normal operating limits ranging from 500 mg/L up to 4500 mg/L, with the lower values corresponding to the higher values of the SVI between day 67 and 87.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 20 40 60 80 100 120Time (days)

TSS

(g/L

)

0

100

200

300

400

500

600

700

SVI (

mL/

g)

TSSSVI

Figure 3.1 – Sludge Volume Index and Total Suspended Solids throughout the survey.



3.1.2.2 MORPHOLOGICAL PARAMETERS MONITORING

With the aim of understanding the morphological changes during the activated sludge survey the first step of this study was devoted to the assessment of the aggregates growth. Therefore, the Total Aggregates Area and Aggregates Equivalent Diameter were determined and their evolution throughout this survey illustrated in Figure 3.2 and Figure 3.3 respectively. The Total Aggregates Area evolution presented a similar behaviour to the TSS, especially from day 25 until the end of the survey. This fact may point that the knowledge of the total area may allow retrieving precious indications on the Total Suspended Solids contents.

0

0.5

1

1.5

2

2.5

0 20 40 60 80 100 120Time (days)

Tota

l Are

a (m

m2/

µL)

Figure 3.2 – Total Aggregates Area throughout the survey.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 20 40 60 80 100 120Time (days)

Eq D

iam

(mm

)

Figure 3.3 – Aggregates Equivalent Diameter throughout the survey.



The results of the Aggregates Equivalent Diameter indicate an increase in the aggregates size from the beginning of the survey until day 10 and a decreasing trend from that day until the end of the survey as shown in Figure 3.3. However, this analysis does not allow to differentiate between the different types of aggregates, namely between the different size classes. Consequently a new approach was made to the morphological data processing taking into account the different size classes and the analysis of each one separately. With this purpose three different subsets of aggregates were studied: aggregates ranging from an Equivalent Diameter of 0.0184 mm up to 0.1 mm, further on designated as 0.02-0.1 mm; aggregates ranging from an Equivalent Diameter of 0.1 mm up to 1 mm, further on designated as 0.1-1 mm; and finally aggregates with Equivalent Diameter larger than 1 mm, further on designated as sup 1 mm. Taking this new approach into consideration, the Aggregates Equivalent Diameter for all the surveyed classes is presented in Figure 3.4.

0.01

0.1

1

10

0 20 40 60 80 100 120Time (days)

Eq. D

iam

. (m

m)

0.02-0.1

0.1-1sup 1

Figure 3.4 – Aggregates Equivalent Diameter throughout the survey.



From the analysis of the Aggregates Equivalent Diameter results, it was clear that for all the survey time each class suffered a decrease throughout the time with the exception for the 0.1-1 mm diameter class from day 101 until the end.

The survey of the quantity of each size class was then performed and is shown in Figure 3.5. As it can be concluded, the predominant class of aggregates throughout this survey was the 0.02-0.1 mm diameter aggregates. Although not so markedly the 0.1-1 mm diameter class was also present in considerable numbers, whereas the larger sup 1 mm aggregates did not attain significant numbers and were almost inexistent from day 46 until the end of the survey. Furthermore, it was possible to relate the increase in the global Aggregates Diameter in the first 10 days with the decrease on the 0.02-0.1 mm diameter class number instead of any size growth in the size classes.

0.01

1

100

10000

0 20 40 60 80 100 120Time (days)

Num

ber (

#/µL

)

0.02-0.1

0.1-1sup 1

Figure 3.5 – Aggregates Number throughout the survey.

The evolution of the quantity of each class of aggregates, in terms of the Aggregates Number Percentage distribution, is shown in Figure 3.6. The predominant classes of



aggregates throughout the survey were the 0.02-0.1 mm followed by the 0.1-1 mm aggregates, whereas the larger sup 1 mm aggregates were practically unexistant. The Number Percentage behaviour of the 0.1-1 mm diameter aggregates was in close agreement with the evolution of the global Equivalent Diameter and the complete opposite of the 0.02-0.1 mm aggregates. However, as this analysis does not reflect the importance of each type of aggregate, as the Area Percentage distribution does, no further conclusions could be reached.

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120Time (days)

Num

ber %

0.02-0.10.1-1sup 1

Figure 3.6 – Aggregates Number Percentage distribution throughout the survey.

The Aggregates Area Percentage distribution is more representative of each size class importance than the Aggregates Number distribution and therefore, is shown in Figure 3.7. The sup 1 mm diameter class area shown for all time less than 5% of the total aggregates area and was practically inexistent from day 46 until the end of the survey. Therefore, the Area Percentage of the 0.02-0.1 mm and 0.1-1 mm diameter classes evolution were the very opposite from each other, but with a clear predominance of the 0.1-1 mm class with values ranging from 60% to 95%, whereas the 0.02-0.1 mm class attained values between 5% and 40%. Analysing Figure 3.7 it is clear a slight increase in the 0.1-1 mm class Area Percentage in the beginning of the survey time until day 12 followed by a decrease until day 87 and a recovery from that day until the end of the survey time in a clear opposition to the behaviour of the 0.02-0.1 mm diameter class.

A more in-depth analysis of these results clarifies the nature of the flocs within the aeration tank. The significant presence of the 0.1-1 mm diameter aggregates with respect to the 0.02-0.1 mm and to the sup 1 mm diameter aggregates seems to point out the predominance of normal flocs instead of pin point flocs or zoogleal flocs. Therefore, the bulking problems within the aerated tank could not be of zoogleal nature, but probably of filamentous nature. Furthermore, the time zone where the Sludge Volume Index presented the higher values fell into the time zone where the aggregates where the smallest throughout the survey, reinforcing the filamentous bulking hypothesis.



0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120Time (days)

Are

a % 0.02-0.1

0.1-1sup 1

Figure 3.7 – Aggregates Area Percentage distribution throughout the survey.

In order to allow a better understanding of the aggregates changes throughout the survey a morphological analysis was performed. With respect to the morphological parameters a total of 7 parameters were studied. From these, three parameters were chosen representing the three major aspects of the aggregates morphology: the ability of the aggregates to take the lesser possible place represented by the Solidity parameter, the elongation of the aggregate by the Eccentricity parameter and finally the roughness of the aggregates edges by the Convexity parameter. Higher values for the Solidity and Convexity parameters and lower values for the Eccentricity parameter indicate a more regular and compact organization. The above mentioned parameters are shown in Figure 3.8, where the results are shown for each of the studied size classes throughout the activated sludge survey.

The values attained for the Solidity parameter were around 0.6 to 0.8 for the 0.02-0.1 mm and 0.1-1 mm diameter classes indicating the formation of somewhat loose structures. For the Eccentricity parameter the values remained around 0.8 for the 0.02-0.1 mm and 0.1-1 mm diameter classes representing elongated structures and from 0.8 to 1 for the larger sup 1 mm aggregates pointing to even more elongated structures. The values of the Convexity parameter achieved values of 0.9 to 1 for the 0.02-0.1 mm diameter class indicating thus the presence of aggregates with smooth edges quite different from the rough edges of the 0.1-1 mm diameter class with values between 0.5 and 0.7. The sup 1 mm aggregates presented a broad range of Solidity and Convexity values, although a predominance of loose and rough edges structures was apparent.



0.1-1

0

0.2

0.4

0.6

0.8

1

Ecce

ntric

ity

0.02-0.1 sup 1

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100 120Time (days)

Con

vexi

ty

0.02-0.1

0 20 40 60 80 100 120Time (days)

sup 1

0 20 40 60 80 100 120Time (days)

0.1-1

0

0.2

0.4

0.6

0.8

1

Solid

ity

0.02-0.1 0.1-1 sup 1

Figure 3.8 – Evolution of the morphological parameters for each size class.

With respect to the free filamentous bacteria, two main aspects were studied: the Filament Length and the Total Filaments Length. The evolution of both of these parameters throughout the survey is shown in Figure 3.9. The free filamentous bacteria length has shown an overall decreasing trend from the beginning until the end of the survey. However, a peak in the length was found between days 67 and 87, corresponding roughly to the period of time in which the SVI values were higher. This fact may point out to a connection amongst the liberation or growth of the filamentous bacteria and the SVI increase, that will be further investigated.



Concerning the total filamentous bacteria contents no clear trend was found in the survey period and instead an oscillating behaviour was observed. Therefore, no significant conclusions could be retrieved by the sole analysis of this parameter.

0

20

40

60

80

100

120

0 20 40 60 80 100 120Time (days)

Fil l

engt

h ( µ

m)

0

20

40

60

80

100

120

140

Tota

l Fil

leng

th (m

m/ µ

L)

Fil lengthTotal Fil length

Figure 3.9 – Filament Length and Total Filaments Length throughout the survey.

Taking into consideration the approach of Sezgin (1982) the Total Filaments Length per Total Suspended Solids Ratio (TL/TSS) was determined as well as the Total Filaments Length per Total Aggregates Area Ratio (TL/TA) which are respectively shown in Figure 3.11 and Figure 3.10.

These parameters are quite useful due to the fact that they allow the determination of the relationship between the free filamentous bacteria presence and the aggregates presence. From the analysis of both figures the most significant information that can be withdrawn is the fact that there is a strong peak in both parameters from day 69 until day 90. This peak correlates roughly with the peak found for the Sludge Volume Index which may point out to a straight dependence of the SVI with the relationship between the free filamentous bacteria and aggregates ratio and therefore, leading towards the existence of a filamentous bulking phenomenon. Furthermore, the TL/TSS exhibit values at all times larger than 10000 mm/mg, which according to Sezgin (1982) indicates the existence of a filamentous bulking problem.



0

50

100

150

200

250

0 20 40 60 80 100 120Time (days)

TL/T

A ra

tio (m

m/m

m2)

Figure 3.10 – Total Filaments Length per Total Aggregates Area Ratio throughout the survey.

0

10000

20000

30000

40000

50000

60000

0 20 40 60 80 100 120Time (days)

TL/T

SS ra

tio (m

m/m

g)

Figure 3.11 – Total Filaments Length per Total Suspended Solids Ratio throughout the survey.

In Figure 3.12 and Figure 3.13 some representative images of the filamentous bacteria and macroscopic aggregates images for a few key days are shown. The chosen key days were: day 1 at the beginning of the experiment; day 47 comprising the beginning of the SVI peak; day 69 comprising the TL/TA, TL/TSS and SVI peaks; day 94 comprising the end of the TL/TA, TL/TSS and SVI peaks; and day 104 at the end of the experiment.



Figure 3.12 – Filamentous bacteria images of some key days in the activated sludge survey (the bar

represents 100 µm).

69

1 47

94

104



Figure 3.13 – Macroscopic aggregates images of some key days in the activated sludge survey (the

bar represents 500 µm).

3.1.2.3 PLS ANALYSIS

Regarding the Partial Least Squares (PLS) analysis in what the Sludge Volume Index is concerned, five major aspects were studied namely the free filamentous bacteria contents, the free filamentous bacteria characterization, the aggregates contents, the aggregates size and the aggregates morphology. The variable importance for each of these aspects was then determined and is presented in Figure 3.14. For the free filamentous bacteria contents the studied parameters were the Total Filaments Length, the Total Filaments Length per Total

69 94

47 1

104



Aggregates Area Ratio and the Filaments Number (Fil Nb), whereas for the free filamentous bacteria characterization the studied parameters were the Filaments Length (FL) and the Filaments Length per Aggregates Area Ratio. Concerning the aggregates contents the studied parameters were the Total Aggregates Area and the Aggregates Number (Floc Nb), whereas for the aggregates morphology the studied parameters were the aggregates Convexity, Compactness, Extent, Solidity, Roundness and Eccentricity. Finally, the aggregates size was represented by the Aggregates Area.

Figure 3.14 – PLS analysis for the SVI on the free filamentous bacteria contents, free filamentous

bacteria characterization, aggregates contents and aggregates morphology.

Analysing these results, the most important parameter with respect to the free filamentous bacteria contents was the TL/TA ratio, for the free filamentous bacteria characterization was the L/A ratio, for the aggregates contents was the Total Area and for the aggregates morphology was the Convexity. A second PLS analysis was then performed with these parameters in order to establish which contributed the most for the Sludge Volume Index. The results of this second PLS analysis are shown in Figure 3.15. Overall, the parameter that was found to contribute the most to the Sludge Volume Index was the Total Filamentous Bacteria Length per Total Aggregates Area Ratio.



Figure 3.15 – Global PLS analysis for the SVI.

The result of the Partial Least Squares analysis between the Sludge Volume Index and the image analysis data emphasized the relationship between the Total Filamentous Bacteria Length per Total Aggregates Area Ratio which is shown in Figure 3.16. The obtained 0.8393 correlation factor however, was far from satisfactory and even more considering the fact that 5 points had to be discarded. Therefore, a linear relationship involving the 5 above-mentioned key aspects of the Sludge Volume Index was determined and the predicted SVI values plotted against the real SVI values as shown in Figure 3.17. Although the obtained 0.8765 correlation factor had improved by the use of this relationship it was still needed to discard 6 points in order to achieve this value. Furthermore, the observed and predicted SVI differed from each other 17.8% when all points were considered, despite of a good agreement (9.2%) when the 6 worst points were discarded.

0

100

200

300

400

500

600

700

800

900

0 50 100 150 200 250

TL/TA (mm/mm2)

SVI (

mL/

g)

Figure 3.16 – Sludge Volume Index vs. Total Filament Length per Total Aggregates Area Ratio.



0

200

400

600

800

1000

0 200 400 600 800 1000SVI pred (mL/g)

SVI o

bs (m

L/g)

Figure 3.17 – Observed vs. predicted Sludge Volume Index.

Moreover, when the SVI values were plotted directly against the Total Filamentous Bacteria Length per Total Suspended Solids Ratio, as shown in Figure 3.18, a 0.8854 correlation factor was obtained with only 3 rejected values. Consequently this parameter seemed to provide the better indication with respect to the Sludge Volume Index values and may be used, at some extent, to monitor the SVI behaviour in a wastewater treatment plant aeration tank.

0

100

200

300

400

500

600

700

0 10000 20000 30000 40000 50000 60000

TL/TSS (m/g)

SVI (

mL/

g)

Figure 3.18 – Sludge Volume Index vs. Total Filament Length per Total Suspended Solids Ratio.

It should be stressed though, that the Sludge Volume Index values throughout this survey were always quite high, ranging from 200 until 620 mL/g. This fact implied that



only the relationship between the image analysis parameters for high SVI values could be established. Therefore, for a wastewater treatment plant working with satisfactory SVI values, i.e. lower than 200 mL/g, these relationships may not stand true. Furthermore, as no points were present in the lower section of the regression line the search of good correlation values may also be hindered.

With respect to the Partial Least Squares (PLS) analysis for the Total Suspended Solids, three major aspects were studied namely the free filamentous bacteria contents, the aggregates contents and the aggregates morphology. The variable importance for each of these aspects was then determined and is presented in Figure 3.19.

Figure 3.19 - PLS analysis for the TSS on the free filamentous bacteria contents, aggregates contents

and aggregates morphology.

For the free filamentous bacteria contents the studied parameters were the Total Filaments Length, the Total Filaments Length per Total Aggregates Area Ratio and the Filaments Number (Fil Nb). Concerning the aggregates contents the studied parameters were the Total Aggregates Area and the Aggregates Number (Floc Nb), whereas for the aggregates



morphology the aggregates Convexity, Compactness, Extent, Solidity, Roundness, and Eccentricity were the studied parameters.

Analysing these results, the most important parameter with respect to the free filamentous bacteria contents was the TL/TA ratio, for the aggregates contents was the Total Area and for the aggregates morphology was the Convexity. A second PLS analysis was then performed with these parameters in order to establish which contributed the most for the Total Suspended Solids. The results of this second PLS analysis are shown in Figure 3.20. Overall, the parameter that was found to contribute the most to the Total Suspended Solids was the Total Aggregates Area.

Figure 3.20 – Global PLS analysis for the TSS.

The result of the Partial Least Squares analysis between the Total Suspended Solids and the image analysis data emphasized the relationship between the Total Aggregates Area which is shown in Figure 3.21. The obtained 0.9335 correlation factor proved to be quite satisfactory considering the fact that only 2 points were discarded. Therefore, it was reasonable to infer that the Total Suspended Solids could be satisfactory monitored by the Total Aggregates Area.

Once again a linear relationship involving the above-mentioned key aspects of the Total Suspended Solids was tested and the predicted TSS values plotted against the real TSS values as shown in Figure 3.22. However, the obtained 0.9223 correlation factor with 2 points discarded was found to be lower than for the relationship between the TSS and the Total Aggregates Area. Furthermore, the observed and predicted TSS differed from each other 15.4% when all points were considered, and 14.3% when the 2 worst points were discarded. Hence, and given the satisfactory correlation factor for the relationship between the Total Suspended Solids and the Total Aggregates Area it was found preferable to use only this parameter to monitor the TSS contents.



0

1

2

3

4

5

0 0.5 1 1.5 2 2.5

TA (mm2)

TSS

(g/L

)

Figure 3.21 – Total Suspended Solids vs. Total Aggregates Area.

0

1

2

3

4

5

0 1 2 3 4 5TSS pred (g/L)

TSS

obs (

g/L)

Figure 3.22 – Observed vs. predicted Total Suspended Solids.

Results and Discussion Anaerobic Wastewater Treatment Processes 3.2



In the anaerobic granulation process monitoring the Equivalent Diameter and Area Percentage values revealed a shift in the aggregates size from the 0.01-0.1 mm to the 0.1-1 mm size class from the beginning of the experiment until day 141 and another shift from the 0.1-1 mm to the sup 1 mm size class during all the second period of the operation. Furthermore, it was possible to establish the aggregates size changes and disturbances caused by the increase of the Up-Flow Velocity and Organic Loading Rate in the third period of the operation. Concerning the morphological parameters for the larger sup 1 mm diameter aggregates class an overall granulation time of 254 days allowed the formation of smooth, compact yet elongated granular structures. The analysis of the TL/TA and TL/VSS allowed for the determination of an initial stage on the granulation process until day 50 when the growth of the free filamentous bacteria contents was clearly predominant. In the second stage from day 50 to day 115 the aggregates growth largely surpassed the filamentous bacteria growth using the filamentous bacteria as a backbone. The third stage of the granulation process took place on the second period of operation and consisted on a balanced growth between the free filamentous bacteria and the aggregates. Moreover, the Up-Flow Velocity and Organic Loading Rate increase in the third period of operation led to a shift of this balance towards the free filamentous bacteria growth.

In the granule deterioration triggered by oleic acid process the results obtained for the outgoing effluent VSS reflected a biomass wash-out phenomenon throughout all the experiment time. Furthermore, the biomass adsorbed substrate was significantly higher in the top section of the reactor than in the bottom section, pointing to lighter aggregates in the top section. Analysing the Equivalent Diameter it was found a size increase for the 0.1-1 mm aggregates alongside a size reduction for the sup 1 mm aggregates reflecting an oleic acid adsorption on the 0.1-1 mm aggregates and a wash-out of the larger aggregates as they become lighter. The results of the Area Percentage illustrate a shift on the aggregates predominant size from the sup 1 mm class to the 0.1-1 mm class with the increase of the Organic Loading Rate especially from day 191, as well as an aggregate stratification with higher values of the sup 1 mm class in the top. The aggregates morphology did not present except for an oscillating behaviour on sup 1 mm aggregates smoothness. With respect to the TL/TA an initial peak was found at day 52, followed by a correction at day 70 and an increasing trend from then until the end of the experience, as well as a clear stratification with much higher values in the top section mainly from day 92 on. These results revealed a shift of the filamentous bacteria from a well packed structure towards a more freely dispersed structure throughout the experiment and their rise to the top section. The correlation between the TL/TA ratio and the Fines Area Percentage allowed to establish the dependence of the filamentous bacteria contents with the aggregates fragmentation up to a value of 40% of Fines. From that point, and due to the selection pressure, the filament contents assumed constant values.

Results and Discussion 3.2 Anaerobic Wastewater Treatment Processes


3.2.1 ANAEROBIC GRANULATION PROCESS MONITORING

The anaerobic granulation process was surveyed by means of image analysis for both the aggregates contents and morphology and the free filamentous bacteria contents. With this purpose the Filaments Length and contents as well as the aggregates contents and 21 different morphological parameters were determined (see Section 2.1.3). The results presented here refer only to the most significant on this survey and therefore, only 3 (Solidity, Eccentricity and Convexity) of the 21 morphological parameters are shown. Also a parameter originating from both the aggregates and filaments contents (TL/TA) was included due to its importance in the enlightenment of the granulation process. Overall, more than 75000 aggregates were studied in this survey. Upon the determination of the trend lines some points had to be discarded and therefore, they appear as grey points instead of black in each figure. Each of the 3 grey areas on the figures represent the 3 different periods of operation as described in Section 2.1.3.


This granulation experiment was divided into three periods of operation. In the first period the reactor was operated with increasing Up-Flow Velocity and Organic Loading Rate and decreasing Hydraulic Retention Times. From the first to the second period of the experiment, there was a loss of the biomass within the reactor so a new biomass inoculum had to be added at the beginning of the second period of operation. Again, in this second period the reactor was operated with increasing Up-Flow Velocity and Organic Loading Rate and decreasing Hydraulic Retention Times. From the second to the third period of operation there was a period during which the biomass was kept in a refrigerator. Finally, in the third period of the experiment, the reactor was submitted to a significant increase in the Up-Flow Velocity and Organic Loading Rate.

The operating parameters such as the Hydraulic Retention Time, Up-Flow Velocity and Organic Loading Rate as well as the Chemical Oxygen Demand (COD) removal percentage were determined by Pablo Araya-Kroff (Araya-Kroff et al., 2002) and Lúcia Neves (Neves, 2002) and are shown in Figure 3.23 and Figure 3.24.

The reactor showed from practically the beginning of the experiment, more precisely from around day 15, a high COD removal efficiency with values surpassing 95%. Only in the period between day 180 and 270 there was a slight decrease on the COD removal percentage.

Regarding the sludge blanket height, it was found to change sudden and drastically with the experiment time and thus the Total Aggregates Area, Volatile Suspended Solids and Total Filament Length within the reactor could not be considered as a fully reliable measure of the aggregates and free filamentous bacteria contents, because they reflected only a partial analysis of the sludge.



0

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60

80

100

120

0 50 100 150 200 250 300 350 400Time (days)

HRT

(h)

0

1

2

3

4

5

6

vUF

(m/h

)

HRTvUF

Figure 3.23 – Hydraulic Retention Time and Up-Flow Velocity throughout the granulation process.

0

1

2

3

4

5

6

7

8

9

0 50 100 150 200 250 300 350 400Time (days)

OLR

(g C

OD

/L.d

)

0102030405060708090100

Rem

oval

(%)

OLRRem

Figure 3.24 – Organic Loading Rate and COD removal percentage throughout the granulation

process.

The effluent Volatile Suspended Solids, shown in Figure 3.25 allowed to establish a heavy biomass wash-out during the first 15 days and a decay until the end of the first period of operation. In the beginning of the second period of operation, when the biomass within the reactor was changed, there was another strong biomass wash-out at day 115, but from that day until the middle of the third period of operation, there was no significant wash-out. With the increase of the Organic Loading Rate however, there seemed to take place a slight wash-out of the biomass. It should be noticed that these values correspond to a period of 8 days in which the biomass was accumulated in the external settler so care must be taken on the analysis of the absolute values.



0

5

10

15

20

25

0 100 200 300 400Time (days)

Efflu

ent V

SS (g

/L)

Figure 3.25 – Outgoing effluent Volatile Suspended Solids throughout the granulation process.

3.2.1.2 DILUTION STUDY

Biomass samples must be diluted for image analysis, but an optimal value should be found. Excessive dilution increases the number of objects detected and if the dilution is insufficient the object will be overlaid. In order to determine the optimal dilutions for the granulation process experiment as well as the dilution factors for the correction of non optimal dilutions the Area Recognition Percentage and the Aggregates Number or Filaments Number per image had to be determined for several dilutions. The dilution factors were then obtained from the ratio between the Aggregates Number or Filaments Number per image of the experimental dilution and of the optimal dilution.

The Area Recognition Percentage was determined as the sum of the areas of the aggregates completely within the image and the Total Aggregates Area, comprising the aggregates cut-off by the borders of the images. Whenever this recognition percentage fell bellow the level of 80% meant that the dilution of the samples should be increased and a new set of dilutions were prepared and the optimal dilution determined. The total Area Recognition Percentages for each day are shown in Figure 3.26.

Although the Area Recognition Percentage for day 399 was bellow the 80% recognition that fact was due not to inappropriate dilutions, but to the presence of excessively large aggregates that could not be fully contained by the image. The abnormal low and high recognition percentages of day 182 and 352 respectively were due to the use of dilutions different from the optimal ones that were corrected by a dilution correction factor.



0

20

40

60

80

100

0 100 200 300 400Time (days)

Reco

gniti

on %

Figure 3.26 – Area Recognition Percentage throughout the granulation process.

From the beginning of the experiment until day 156 the Area Recognition Percentage and the Flocs Number per image without dilution and for the 1:2, 1:5, 1:10 and 1:20 dilutions are shown in Figure 3.27, as well as the Filaments Number per image. The Area Recognition Percentage employed to identify the optimal dilution of 1:20 was determined by the use of the flocs data.

0

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60

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100

0 5 10 15 20Dilution

Floc

s num

ber p

er im

age

0

20

40

60

80

100

Are

a re

cogn

ition

per

cent

age

NumberRec. perc. 0

100

200

300

400

500

0 5 10 15 20Dilution

Fila

men

ts n

umbe

r per

imag

e

Number

Figure 3.27 – Flocs and Filaments Number per image and Area Recognition Percentage.

For the period between day 169 and 309 the Flocs Number per image for the 1:5, 1:10, 1:20, 1:50 and 1:100 dilutions are shown in Figure 3.28, as well as the Filaments Number per image, Granules Number per image and Area Recognition Percentage. The Area Recognition Percentage employed to identify the optimal dilution of 1:50 was determined by the use of the granules data.



0

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0 20 40 60 80 100Dilution

Gra

nule

s num

ber p

er im

age

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Are

a re

cogn

ition

per

cent

age

NumberRec. perc.

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0 20 40 60 80 100Dilution

Floc

s num

ber p

er im

age

Number

0

100

200

300

400

500

0 20 40 60 80 100Dilution

Fila

men

ts n

umbe

r per

imag

eNumber

Figure 3.28 – Flocs, Filaments and Granules Number per image and Area Recognition Percentage.

For the period between day 337 until the end of the experiment the Area Recognition Percentage and the Flocs Number per image for the 1:20, 1:50, 1:100, 1:150 and 1:200 dilutions are shown in Figure 3.29, as well as the Filaments Number per image, Granules Number per image and Area Recognition Percentage. The Area Recognition Percentage employed to identify the optimal dilution of 1:100 was determined by the use of the granules data.

0

1500

3000

4500

6000

7500

9000

0 50 100 150 200Dilution

Gra

nule

s num

ber p

er im

age

0

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Are

a re

cogn

ition

per

cent

age

NumberRec. perc.

0

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0 50 100 150 200Dilution

Floc

s num

ber p

er im

age

Number0

400

800

1200

1600

2000

0 50 100 150 200Dilution

Fila

men

ts n

umbe

r per

imag

e

Number

Figure 3.29 – Flocs, Filaments and Granules Number per image and Area Recognition Percentage.


In order to fully understand the morphological changes occurred during the granulation process the first step of this study was devoted to the assessment of the aggregates growth. The Aggregates Equivalent Diameter were then determined and their evolution throughout the granulation experiment time shown in Figure 3.30. The results of the Aggregates Equivalent Diameter were inconclusive, with a somewhat oscillating behaviour throughout the experiment time as shown in Figure 3.30. Therefore, a new approach was made to the morphological data processing, taking into account the



different size classes and the analysis of each one separately. With this purpose four different subsets of aggregates were studied: aggregates ranging from an Equivalent Diameter of 0.00384 mm (minimal size of the aggregates recognized by the image analysis program) up to 0.01 mm, further on designated as inf 0.01 mm; aggregates ranging from an Equivalent Diameter of 0.01 mm up to 0.1 mm, further on designated as 0.01-0.1 mm; aggregates ranging from an Equivalent Diameter of 0.1 mm up to 1 mm, further on designated as 0.1-1 mm; and finally aggregates with Equivalent Diameter larger than 1 mm, further on designated as sup 1 mm.

0

0.005

0.01

0.015

0.02

0.025

0 100 200 300 400Time (days)

Eq D

iam

(mm

)

Figure 3.30 – Aggregates Equivalent Diameter throughout the granulation process.

The Aggregates Equivalent Diameter for each size class were then determined and their evolution throughout the granulation experiment time is shown in Figure 3.31. With respect to the Aggregates Equivalent Diameter of the inf 0.01 mm and 0.01-0.1 mm size classes no significant changes were remarked. Analyzing further Figure 3.31, an increase on the 0.1-1 mm aggregates Aggregates Equivalent Diameter is clear from day 1 until day 295, starting of a value of 0.14 mm and reaching a value of 0.41 mm. It was also noticeable a stronger size increase all along the second period of operation corresponding to a higher growth of these aggregates. From day 295 until the end of the operation there was first a slight decrease and then a recovery in the Aggregates Equivalent Diameter. The sup 1 mm aggregates, on the other hand showed an overall increase in the Equivalent Diameter reaching a final value of 1.72 mm with a slight decrease in the beginning of the third period until day 295. This Equivalent Diameter data for each aggregates range allowed to further elucidate the increase of each of the size classes size throughout the granulation experiment time.

From this results it is clear the growth of the 0.1-1 mm diameter aggregates in the first 295 days especially in the second period of operation. It is also possible to discern the overall growth of the sup 1 aggregates throughout the experiment. However, with the sharp increase of the Up-Flow Velocity in the beginning of the third period of operation an initial decrease in their Equivalent Diameter was noticeable. With respect to the Equivalent



Diameter of the smaller 0.01-0.1 mm diameter aggregates little change could be found along the granulation experiment time remaining thus approximately constant.

0.001

0.01

0.1

1

10

0 100 200 300 400Time (days)

Eq. D

iam

. (m

m)

0.01-0.10.1-1sup 1inf 0.01

Figure 3.31 – Aggregates Equivalent Diameter throughout the granulation process.

The evolution of the quantity of each class of aggregates, in terms of the Aggregates Number Percentage distribution, is shown in Figure 3.32. The predominant classes of aggregates throughout the anaerobic granulation experiment were the inf 0.01 mm followed closely by the 0.01-0.1 mm aggregates. Both these aggregates, smaller than 0.1 mm in Equivalent Diameter, attained a cumulative value above 94% throughout all the experiment time, whereas the aggregates larger than 0.1 mm only achieved a maximum of 6%. Analyzing the behaviour of the Number Percentage of the inf 0.01 mm diameter aggregates it was noticeable an oscillating trend with values ranging from 39% to 81%. The Number Percentage of the 0.01-0.1 mm diameter aggregates ranged from 19% to 60% and started by decreasing from day 1 until day 37 followed by an increase from that day until day 115. In the second period of operation their Number Percentage steadily decreased, but with the increase of the Up-Flow Velocity and Organic Loading Rate a peak was found. The Number Percentage of the 0.1-1 mm diameter aggregates shown an almost parallel trend from the 0.01-0.1 mm class, but with values seemingly lower ranging from 0.05% to 6%. The exception was the last period of operation from day 337 until the end of the experiment where its Number Percentage increased. Concerning the larger aggregates



their Number Percentage was always bellow 0.1% and showed an increasing trend starting from day 156 throughout the experiment time, with the exception of the beginning of the third period from day 254 until day 337 where it decreased slightly.

Analyzing furthermore these results two key conclusions could be reached: from day 156 until the end of the second period of operation there was a steep decrease on the Number Percentage of the 0.1-1 mm diameter aggregates corresponding to the increase on the larger sup 1 aggregates and with the exception of the beginning of the third period where the Up-Flow Velocity and Organic Loading Rate were significantly increased the larger aggregates showed an overall increasing trend in their Number Percentage starting from day 156. It must be emphasized though, that this analysis does not reflect the importance of each type of aggregate within the reactor, as the Area Percentage distribution does, on one hand, and that it is quite dependent on the number of the smaller inf 0.01 mm aggregates, on the other.

0.001

0.01

0.1

1

10

100

0 100 200 300 400Time (days)

Num

ber %

inf 0.010.01-0.10.1-1sup 1

Figure 3.32 – Aggregates Number Percentage distribution throughout the granulation process.

The Aggregates Area Percentage distribution, shown in Figure 3.33, is more representative of each size class importance in the granulation process than the Aggregates Number Percentage distribution.



0

20

40

60

80

100

Are

a % 0.01-0.1

0

20

40

60

80

100

Are

a % 0.1-1

0

20

40

60

80

100

0 50 100 150 200 250 300 350 400Time (days)

Are

a %

sup 1

0

20

40

60

80

100

Are

a % inf 0.01

Figure 3.33 – Aggregates Area Percentage distribution throughout the granulation process.

In opposition to the Number Percentage, the Area Percentage of inf 0.01 mm aggregates was, overall, the less significant of all the size classes, being at all times lesser than 15%. No significant changes were noticeable for the inf 0.01 mm diameter class except for small increases with the increases in the Up-Flow Velocity and Organic Loading Rate. The 0.01-0.1 mm aggregates Area Percentage ranged from 10% to 75%, with an initial decreasing trend from day 1 to 141 to a value around 25% and then presenting little change until the beginning of the third period of operation. At day 267 a strong peak was found corresponding to the increase on the Up-Flow Velocity, and from day 281 until day 337 a sharp increase in the Area Percentage was noticeable corresponding to the increase on the Organic Loading Rate. Finally, at the end of the experiment the Area Percentage value fell



to a value of 10%. The 0.1-1 mm aggregates Area Percentage ranged from 15% to 85%, with an initial increasing trend from day 1 to 141 and then presenting a stable descending behaviour down to a value of 15%until day 309. At day 281, shortly after the increase on the Up-Flow Velocity, a strong increase in the Area Percentage could be found followed by a decrease to the previous values until day 337 and a recovery from that day until the end of the experiment to a value of 25%. The sup 1 mm aggregates showed residual values until day 156 and an increasing trend from then until day 254 at the beginning of the third period of operation. With the increase in the Up-Flow Velocity, however, there was a strong decrease on the Area Percentage, until day 309. With the increase on the Organic Loading Rate the sup 1 mm aggregates Area Percentage started to rise up once again and until the end of the experiment when attained values of 65%.

A more in-depth analysis of the Aggregates Area Percentage values, allows the conclusion that there was a shift of the aggregates size in the first 141 days from the range of 0.01-0.1 mm to the 0.1-1 mm diameter aggregates. Furthermore, from day 156 until the end of the second period of the operation there was another shift in the aggregates size but this time, from the 0.1-1 mm to the 1 mm diameter range as also suggested by the Aggregates Equivalent Diameter results. With the increase of the Up-Flow Velocity however the larger aggregates Area Percentage suffered a strong decrease and with the increase of the Organic Loading Rate a notorious shift in the Area Percentage from the 0.1-1 mm to the 0.01-0.1 mm was evident. Nevertheless, at the end of the third period of operation the 0.1-1 mm aggregates started to recover corresponding also to a decrease in the 0.01-0.1 mm class Area Percentage. In conclusion, these results clearly illustrate the aggregates growth throughout the granulation experiment time as well as the aggregates changes and disturbances caused by the sharp increases in both the Up-Flow Velocity and the Organic Loading Rate.

In order to allow a better understanding of the aggregates changes throughout the experiment a morphological analysis was performed and a total of 21 parameters were studied. From these, three parameters were chosen representing the three major aspects of the aggregates morphology: the ability of the aggregates to take the lesser possible place represented by the Solidity parameter, the elongation of the aggregate by the Eccentricity parameter and finally the roughness of the aggregates edges by the Convexity parameter. Higher values for the Solidity and Convexity parameters and lower values for the Eccentricity parameter indicate a more regular organization and, therefore, a more granular aggregate. The above mentioned parameters are shown in Figure 3.34, where the results are shown for each of the studied size classes throughout the granulation experiment time with the obvious exception of the larger aggregates not present in the first 156 days. Furthermore, the smaller of the studied aggregates with a maximum diameter of 0.01 mm or 10 µm can not be considered as true flocs due to the small number of aggregated bacteria but as residual flocs and, therefore, are not shown in these results.



0

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vexi

ty

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ity

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sup 10.1-1

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Ecce

ntric

ity

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0 100 200 300 400Time (days)

sup 1

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0.1-1

0.1-1 sup 1

Figure 3.34 – Evolution of the morphological parameter for each size class.

A general analysis of the results shows a decrease in the Solidity and Convexity and therefore, a decrease in granule similitude, for the 0.01-0.1 mm and 0.1-1 mm size classes during the first 50 days, followed by an opposite trend from then until the end of the second period of the operation and the beginning of the third. At that time the Solidity and Convexity reached their maximum values maintaining them practically unaltered throughout the increase of Up-Flow Velocity and the Organic Loading in the third period of operation. The Eccentricity values showed no significant changes for the 0.01-0.1 mm and 0.1-1 mm size classes although an opposite behaviour from the above mentioned parameters could be perceived. The overall results of the larger size class presented an



increase in the Solidity and Convexity and a decrease in the Eccentricity, and therefore, an increase in granule similitude, until day 254. In the third period of operation with the increase of the Organic Loading Rate an oscillating behaviour seemed to have occurred. However, the 95% confidence intervals on this sup 1 mm size class was significantly higher than the ones from the smaller size classes as a consequence of the smaller number of analysed aggregates. The maximum values attained for the larger sup 1 mm aggregates with respect to the Solidity parameter were of 0.9 indicating the formation of compact structures. For the Eccentricity parameter the minimum value was 0.6 pointing to elongated structures whereas the values of the Convexity parameter achieved values of 0.8 indicating thus the presence of aggregates with somewhat smooth edges. The quite sharp increase for the Convexity parameter for the 0.1-1 mm aggregates at day 169 was partially due to the fact that these aggregates were, from that day on and until the end of the experiment, obtained from the macroflocs images instead of the microflocs images as before.

The analysis of the larger sup 1 mm diameter size class points out to a granulation time of roughly 254 days where the Solidity and Convexity parameters achieved the higher values, the Eccentricity was lower, and hence the granule similitude was higher. Therefore, this day was considered the granulation time for this experiment in what the morphological parameters are concerned. As pointed out by the Solidity, Eccentricity and Convexity values this granulation time allowed for the formation of somewhat smooth, compact yet elongated granular structures.

In order to further enlighten the granule deterioration process the Filament Length during the time of this experiment was surveyed and is next presented in Figure 3.35.

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120

140

0 100 200 300 400Time (days)

Fil l

engt

h ( µ

m)

Figure 3.35 – Filament Length throughout the granulation process.

A strong increase in the Filament Length is apparent from 35 µm in day 1 up to 80 µm in day 77. From that day until day 156 the Filaments Length slightly decreased but, from that day until the beginning of the third period of operation recovered to a value of 45 µm. The increase of the Up-Flow Velocity in the beginning of the third period of operation provoked a peak in the Filaments Length at day 267, and starting from the



increase on the Organic Loading Rate at day 309 the length kept rising to a final value of 105 µm.

The Total Filaments Length per Total Aggregates Area Ratio (TL/TA) and the Total Filaments Length per Volatile Suspended Solids Ratio (TL/VSS) are shown in Figure 3.36 and Figure 3.37 respectively. These are very helpful parameters due to the fact that allow the quantification of the relationship between the free filamentous bacteria presence and the aggregates presence.

0

50

100

150

200

250

300

0 100 200 300 400Time (days)

TL/T

A r

atio

(mm

/mm

2)

Figure 3.36 – Total Filaments Length per Total Aggregates Area Ratio throughout the granulation

process.

0

50000

100000

150000

200000

250000

300000

350000

400000

0 100 200 300 400Time (days)

TL/V

SS r

atio

(m/g

VSS

)

Figure 3.37 – Total Filaments Length per Volatile Suspended Solids Ratio throughout the granulation

process.



From the analysis of these results an increase in the TL/TA and TL/VSS ratios are observed from day 1 to day 50 meaning that the free filamentous bacteria growth is overlapping the aggregates growth. From that day until day 115 there is a much higher increase on the aggregates contents than in the free filamentous bacteria contents. The second period of operation revealed a balanced growth between aggregates and filamentous bacteria with nearly constant values. As observed for the free filamentous bacteria contents, the increase of the Up-Flow Velocity and of the Organic Loading Rate caused a peak in the TL/TA and TL/VSS ratios at day 267 whereas the increase of the Organic Loading Rate caused a peak at days 352 and 337 respectively.

There are a few major conclusions that can be withdrawn by a more profound analysis of these parameters as follows: the first stage of the granulation process (from day 1 to day 50) revealed a higher growth of the free filamentous bacteria than the aggregates. This conclusion is also supported by the results of the morphological parameters of the aggregates during this period (see Figure 3.34) in which they became more irregular and less compact. This first stage was followed by a clear inversion from day 50 to day 115 where the aggregates growth largely surpassed the free filamentous bacteria growth and the aggregates became to show an inversion in their morphology towards a more regular, compact and therefore, granule similitude. In the second period of operation there seems to be a balanced growth between both. Furthermore, that is the period where the TL/TA and TL/VSS ratio values are minimum leading to a more granular structure than the previous ones. These results corroborate the assumption that the granulation process could have been fully completed in 254 days as shown by the low values of the Total Filaments per Total Area Ratio. Moreover, the increase of the Up-Flow Velocity and of the Organic Loading Rate seemed to have caused some granule disruption, with liberation of filamentous bacteria from the aggregates emphasized by the fact that the larger aggregates morphology presented an irregular behaviour in this period.

In Figure 3.38, Figure 3.39 and Figure 3.40 some representative images of the filamentous bacteria and microscopic and macroscopic aggregates images for a few key days are shown. With respect to the absence of the macroscopic aggregates images in the first three key days that was due to the fact that no macroscopic images were acquired until day 169. The chosen key days were: day 1 at the beginning of the experiment; day 50 comprising the TL/TA and TL/VSS peaks and higher aggregates irregularity; day 115 at the beginning of the second period of operation; day 254 at the granulation time and the beginning of the third period of operation; day 352 comprising the TL/TA and TL/VSS peaks due to the Organic Loading Rate increase; and day 399 at the end of the experiment.



Figure 3.38 – Filamentous bacteria images of some key days in the granulation experiment (the bar represents 100 µm).

115 254

1 50

352 399



Figure 3.39 – Microscopic aggregates images of some key days in the granulation experiment (the bar represents 100 µm).

1 50

115

352

254

399



Figure 3.40 – Macroscopic aggregates images of some key days in the granulation experiment (the

bar represents 500 µm).

254 352

399



3.2.2 GRANULE DETERIORATION TRIGGERED BY OLEIC ACID

In this work the granule deterioration process by the step increase in the oleic acid loading rate was surveyed by means of image analysis of both the aggregates contents and morphology and the free filamentous bacteria contents. With this purpose the Filaments Length and contents as well as the aggregates contents and 21 different morphological parameters were determined (see Section 2.1.4). The difference between the aggregates and free filamentous bacteria contents in the overall and both the top and bottom sections of the reactor was studied in order to enlighten the possible stratification within the reactor. The results presented here refer only to the most significant on this survey and therefore, only 3 (Solidity, Eccentricity and Convexity) of the 21 morphological parameters are shown. Also a parameter originating from both the aggregates and filaments contents (TL/TA) was included due to its importance in the enlightenment of the granule deterioration process. Overall more than 30000 aggregates were studied in this survey, with an optimal dilution of 1:20 as identified by the Area Recognition Percentage higher than 97.5% throughout the experiment for this dilution. For the determination of the trend lines some points had to be discarded and therefore, they appear as grey points instead of black in each picture. Each of the 4 grey areas on the figures represent the 4 different periods of operation as described in Section 2.1.4.


This granule deterioration experiment was divided in four different periods accordingly to the feed composition. In the first period of operation the reactor was fed with skim milk and oleic acid in equal percentages of COD. In the second period of operation the total Organic Loading Rate was maintained but the composition of the substrate shifted to oleic acid as the sole carbon source. In the beginning of the third and fourth periods of operation the Organic Loading Rate was increased. The Hydraulic Retention Time and Up-Flow Velocity were maintained with constant values of 1 day and 0.2 m/h, respectively, throughout all the experiment time. The operating parameters Organic Loading Rate and Chemical Oxygen Demand (COD) Removal Percentage were determined by Alcina Pereira (Pereira et al., 2001) and are shown in Figure 3.41. Although only samples from the top and bottom sections of the reactor were taken, an overall estimation of the different surveyed parameters was performed using the top and bottom values and their comparative numbers to interpolate the overall behaviour of the reactor.

With respect to the biomass within the reactor, the sludge blanket height was found to change sudden and drastically with the experiment time. Hence, the Total Aggregates Area, Volatile Suspended Solids and Total Filament Length parameters could not be considered as a fully reliable measure of the aggregates and free filamentous bacteria contents, because it reflected only a partial analysis of the sludge, mainly in the top section of the reactor.

However, the results obtained for the outgoing effluent Volatile Suspended Solids (again determined by Alcina Pereira (Pereira et al., 2001)) shown in Figure 3.42 allowed to conclude that the biomass within the reactor suffered a wash-out phenomenon throughout all the experiment time with particular emphasis after each Organic Loading Rate increase. Furthermore, the adsorbed substrate onto the biomass was in average 144%



higher in the top section of the reactor than in the bottom section, which may point to lighter aggregates in the top section.

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9

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OLR

(gC

OD

/L.d

ay)

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50

60

70

80

90

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CO

D re

mov

al

OLRCOD removal

Figure 3.41 – Organic Loading Rate and COD Removal Percentage throughout the experiment time.

0

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3

4

5

0 50 100 150 200 250Time (days)

Efflu

ent V

SS (g

/L)

Figure 3.42 – Outgoing effluent Volatile Suspended Solids throughout the experiment time.

It must be emphasized though, that the results presented here refer only to the values of each particular day. For the COD Removal Percentage and Volatile Suspended Solids these values differ drastically on a daily basis and therefore additional care should be taken on analysing these results.




In order to better understand the morphological changes occurred during the granule deterioration process the Equivalent Diameters were determined and their evolution throughout the experiment time shown in Figure 3.43.

0

0.05

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0.15

0.2

0.25

0.3

0.35

0 50 100 150 200 250 300Time (days)

Eq. D

iam

. (m

m)

OverallTopBottom

Figure 3.43 – Aggregates Equivalent Diameter throughout the experiment time.

Analysing Figure 3.43 no solid conclusions could be withdrawn apart from the fact that it was noticeable an oscillating behaviour throughout the experiment time and therefore, these results per se were found to be inconclusive. Therefore, a new approach was made to the morphological data processing taking into account the different size classes and the analysis of each one separately. With this purpose three different subsets of aggregates were studied: aggregates ranging from an Equivalent Diameter of 0.05203 mm (minimal size of the aggregates recognized by the image analysis program) up to 0.1 mm, further on designated as 0.05-0.1 mm; aggregates ranging from an Equivalent Diameter of 0.1 mm up to 1 mm, further on designated as 0.1-1 mm; and finally aggregates with Equivalent Diameter larger than 1 mm, further on designated as sup 1 mm.

The evolution of the Equivalent Diameter for each class of the aggregates throughout the experiment time is provided in Figure 3.44. For the larger sup 1 mm aggregates there was an overall slight size reduction whereas for the 0.1-1 mm diameter aggregates the inverse was true, i. e. an overall size raise can be noticed. The smaller 0.05-0.1 mm diameter aggregates maintained a steady size throughout all of the experiment. Furthermore, the sup 1 mm and 0.1-1 mm diameter aggregates size changes are more significant in the bottom section of the reactor, closer to the feed inlet, than in the top section of the reactor. This increase on the Equivalent Diameter of the 0.1-1 mm diameter class can be explained by the oleic acid adsorption on the aggregates making them larger, whereas the slight size decrease in the sup 1 mm diameter class may be the result of the larger aggregates wash-out as they become lighter.



The comparison among the aggregates size between the top and the bottom section of the reactor did not present major differences between the two sections. However, for the 0.1-1 mm diameter class the bottom section still presented an average 8% larger values than the top, whereas for the larger sup1 mm diameter aggregates the inverse was true with an average 8% larger values for the top section of the reactor. Furthermore, from day 70 until the end of the experiment the difference between top and bottom sections for the sup 1 mm diameter class presented a constant increasing trend. Relating these results with the fact that in the top section of the reactor the aggregates presented higher adsorbed substrate (oleic acid) quantities, it may be inferred that these larger aggregates became lighter and therefore, migrated to the top section of the reactor probably due to the effect of the adsorbed oleic acid.

0 100 200 300Time (days)

0.05-0.10.1-1sup 1

Overall

0 100 200 300Time (days)

0.05-0.10.1-1sup 1

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0.01

0.1

1

10

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Eq. D

iam

. (m

m)

0.05-0.10.1-1sup 1

Bottom

Figure 3.44 – Aggregates Equivalent Diameter throughout the experiment time.

The evolution of the quantity of each class of aggregates, in terms of the Aggregates Number Percentage distribution, is shown in Figure 3.45. The predominant classes of aggregates throughout the experiment time were the 0.05-0.1 mm and the 0.1-1 mm aggregates, whereas the larger sup 1 mm aggregates did not surpass 7.1%. Furthermore, it was found an oscillating increasing trend for the 0.1-1 mm size class for both sections and the overall reactor and an opposite behaviour in what the 0.05-0.1 mm size class is concerned. Although the values of the Number Percentage practically did not varied for the sup 1 mm aggregates, still a slightly decreasing trend could be perceived.

Once again, it must be stressed that this analysis does not reflect the importance of each type of aggregate within the reactor, as the Area Percentage distribution does, and therefore, no further conclusions could be reached.



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Num

ber %

0.05-0.10.1-1sup 1

Bottom

0 100 200 300Time (days)

0.05-0.10.1-1sup 1

Top

0 100 200 300Time (days)

0.05-0.10.1-1sup 1

Overall

Figure 3.45 – Aggregates Number Percentage distribution throughout the experiment time.

The Aggregates Area Percentage distribution, shown in Figure 3.46, is more representative of each size class importance in the granule deterioration process than the Aggregates Number Percentage distribution.

0 100 200 300Time (days)

0.05-0.10.1-1sup 1

Overall

0 100 200 300Time (days)

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Top

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Are

a %

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Bottom

Figure 3.46 – Aggregates Area Percentage distribution throughout the experiment time.

The overall sup 1 mm size class Area Percentage ranging from 80% to 40% showed a decreasing trend from the beginning until the end of the experiment with a sharp decrease from day 191 until the end of the experiment. This trend was also found in both sections of the reactor although in the top section the values were normally higher than in the bottom and the last decrease was steeper. Regarding the 0.1-1 mm diameter aggregates the behaviour was quite the opposite of the sup 1 mm aggregates ranging



from 20% to 65%. The 0.05-0.1 mm diameter aggregates were not significant in terms of the Area Percentage with values always bellow 5%. These results clearly illustrate the shift of the aggregates predominant size with the increase of the Organic Loading Rate and especially from day 191, with a clear shift of the aggregates from the sup 1 mm class to the 0.1-1 mm diameter class.

Comparing the aggregates size between the top and the bottom section of the reactor it was clear that, with the exception of the last day, in the bottom section the Area Percentage of the 0.1-1 mm diameter class was in average 13% higher than in the top section and, inversely, for the sup1 mm diameter class was in average 6% higher in the top than in the bottom. Conversely, this results points to an aggregate stratification within the reactor with higher contents of the larger aggregates in the top section of the reactor. These results also stress the aforementioned conclusion that, with the increase on the adsorbed oleic acid, the aggregates became lighter and rose to the top of the reactor.

In order to allow for a better understanding of the aggregates changes throughout the experiment a morphological analysis was performed and a total of 21 parameters were studied. As for the granulation process, three parameters were chosen representing the three major aspects of the aggregates morphology: the ability of the aggregates to take the lesser possible place represented by the Solidity parameter, the elongation of the aggregate by the Eccentricity parameter and finally the roughness of the aggregates edges by the Convexity parameter. Once again, higher values for the Solidity and Convexity parameters and lower values for the Eccentricity parameter indicate a more regular organization and, therefore, a more granular aggregate. The evolution of these parameters for the top section, the bottom section, and the overall reactor is shown in Figure 3.47.

No significant changes in the aggregates morphology were found for the different classes in both the overall, top and bottom sections of the reactor with two exceptions. The first one is related to the 0.1-1 mm diameter class which seemed to become slightly more compact and regular and the second with the Convexity of the larger sup 1 mm diameter class showing some oscillations during all the experiment time. The values attained for the larger sup 1 mm aggregates for the Solidity parameter were around 0.9 indicating the formation of compact structures. For the Eccentricity parameter the values were in the order of 0.6 to 0.7 pointing to elongated structures whereas the values of the Convexity parameter attained values of 0.8 to 0.9 indicating thus the presence of aggregates with somewhat smooth edges. However, and contrary to the Eccentricity and Solidity parameters, the larger sup 1 mm aggregates were found to present a higher irregularity of the borders than the other two classes.

Comparing the morphological parameters for the aggregates from the top to the bottom section of the reactor again no significant changes were found with differences remaining for all cases bellow 2%.



0.05-0.10.1-1sup 1

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Figure 3.47 – Evolution of the morphological parameters for each size class.

In order to further enlighten the granule deterioration process the free filamentous bacteria average length during the time of this experiment was surveyed and is next presented in Figure 3.48. The Filament Length in the bottom section of the reactor, closer to the feed inlet, has shown a peak for the first period of the operation and then an oscillating slight decrease trend from day 52 until the end of the experiment. A similar behaviour was detected for the overall reactor, although not as pronounced as in the bottom section. In the top section of the reactor, apart from an initial rise on the Filament Length at day 52 followed by a correction at day 70 one could conclude that no really



significant changes have occurred from that day until the end of the experiment. Up to this point, the stand alone analysis of these results could not lead to further conclusions.

The comparison among the free filamentous bacteria length between the top and the bottom section of reactor presented an average 8% larger values for the top than the bottom denoting thus a greater exposure of the filamentous bacteria outside the aggregates.

0 100 200 300Time (days)

Overall0

20

40

60

0 100 200 300Time (days)

Fil l

engt

h ( µ

m)

Bottom

0 100 200 300Time (days)

Top

Figure 3.48 – Filament Length throughout the experiment time.

The Total Filaments Length per Total Aggregates Area Ratio (TL/TA) parameter is shown in Figure 3.49. This is a very helpful parameter due to the fact that allows for the quantification of the relationship between the free filamentous bacteria presence and the aggregates presence. From the analysis of Figure 3.49 it is clear a steep increase of the overall TL/TA ratio in the beginning of the experiment until day 52, followed by a correction at day 70 and an increasing trend from then until the end of the experience, but with particular emphasis until day 141. This same behaviour was found for both the top and bottom sections of the reactor, although in the top section the TL/TA ratio increase was significantly higher and that the correction in the bottom section of the reactor only took place at day 119.

There are a few major conclusions that can be withdrawn by a more profound analysis of the TL/TA parameter: there was either liberation of the filamentous bacteria from the aggregates or an increased growth of the filamentous bacteria in the reactor with the increase of the Organic Loading Rate, leading to a shift of the filamentous bacteria within the reactor from a well packed structure towards a more freely dispersed structure. For the bottom type of the reactor this shift was found to be not as pronounced as in the top section.

The comparison among the Total Filaments Length per Total Aggregates Area Ratio between the top and the bottom sections of the reactor was found to be significantly higher for the top section especially from day 92 until the end of the experiment. Indeed, the average difference between these two sections attained the value of 97%. Keeping in mind the aggregates stratification within the reactor with higher contents of the larger aggregates in the top section of the reactor, these results also enlighten the stratification of the relationship between the free filamentous bacteria and the aggregates with much higher values in the top section of the reactor. In conclusion it seems licit to infer that,



with the increase on the adsorbed oleic acid, the aggregates become lighter, with more freely dispersed structure in terms of the free filamentous bacteria and ultimately rose to the top of the reactor.

0

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0 100 200 300Time (days)

TL/T

A ra

tio (m

m/m

m2)

Bottom

0 100 200 300Time (days)

Top

0 100 200 300Time (days)

Overall

Figure 3.49 – Total Filaments Length per Total Aggregates Area Ratio throughout the experiment time.

In Figure 3.50, Figure 3.51, Figure 3.52 and Figure 3.53 some representative images of the filamentous bacteria and macroscopic aggregates images respectively are shown for a few key days. The chosen key days were: day 1 at the beginning of the experiment; day 70 at the beginning of the second period of operation; day 119 at the beginning of the third period of operation; day 162 at the beginning of the fourth period of operation; and day 219 at the end of the experiment.



Figure 3.50 – Bottom filamentous bacteria images of some key days in the granule deterioration

experiment (the bar represents 100 µm).

70

119 162

1

219



Figure 3.51 – Top filamentous bacteria images of some key days in the granule deterioration

experiment (the bar represents 100 µm).

70

119 162

1

219



Figure 3.52 – Bottom macroscopic aggregates images of some key days in the granule deterioration

experiment (the bar represents 1 mm).

119 162

70 1

219



Figure 3.53 – Top macroscopic aggregates images of some key days in the granule deterioration

experiment (the bar represents 1 mm).

119 162

70 1

219



3.2.2.3 FINES FRACTION MONITORING

When a process of granule deterioration occurs, an interesting analysis is the determination of the Fines and Non-Fines fractions. The aggregates denominated as Fines represent the ones that are able to pass through a 1 mm diameter needle, which was the case for the weight percentage determination, whereas the Non-Fines correspond to larger aggregates. Therefore, in the calculation of the Fines Area Percentage the aggregates belonging to the Fines class were the ones that presented a maximum Width of 1mm. The evolution of the Fines Area Percentage and Fines Weight Percentage throughout the experiment time is shown in Figure 3.54.

Regarding the overall Fines Area Percentage it was noticeable a slight initial decrease until day 70, corresponding to a wash-out of the smaller aggregates. From then until the end of the experiment the Fines Area Percentage continuously increased, in accordance to the afore-mentioned conclusions. The overall Fines Weight Percentage demonstrated a similar behaviour to the Fines Area Percentage with the exception of the last day where it suffered a slight decrease.

0

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40

60

80

100

0 50 100 150 200 250Time (days)

Perc

enta

ge

Fines Weigth %Fines Area %

Figure 3.54 – Fines Area and Weight Percentages distribution throughout the experiment time.

The correlation between the Fines Area Percentage and the Fines Weight Percentage is presented in Figure 3.55. Although a point had to be discarded, there seems to be a satisfactory correlation between the Fines Area Percentage and Fines Weight Percentage. However, these results can not be considered as fully conclusive and a more profound study should be implemented.

The correlations between the Total Filaments Length per Total Aggregates Area Ratio and the Fines Area Percentage for the top and bottom sections of the reactor are presented in Figure 3.56. From the analysis of these results it seems that, especially for the top section of the reactor, the filamentous bacteria contents within the reactor was dependent on the fragmentation of the aggregates to a certain extent (around a value of 40% of Fines) and became independent from that point on. This behaviour could be explained by the selection pressure action on the filaments leading to a wash-out of the non-attached



filaments, and therefore keeping the filamentous bacteria contents in the reactor approximately invariant for high Fines percentages. However, further studies should be made in order to further enlighten this relationship.

0

20

40

60

80

100

0 20 40 60 80 100Fines Weigth %

Fine

s Are

a %

Figure 3.55 – Correlation between the Fines Area Percentage and the Fines Weight Percentage.

0

50

100

150

200

250

0 20 40 60 80 100Fines Area Percentage

TL/T

A (m

m/m

m2)

BottomTop

Figure 3.56 – Correlations between the TL/TA and the Fines Area Percentage for the top and bottom

sections of the reactor.



209

4 CONCLUSIONS AND RECOMMENDATIONS

210

4.1 Aerobic Wastewater Treatment Processes 211 4.1.1 Protozoa and metazoa identification 212 4.1.2 Activated sludge monitoring 214 4.1.3 Recommendations 216 4.2 Anaerobic Wastewater Treatment Processes 219 4.2.1 Anaerobic granulation process monitoring 220 4.2.2 Granule deterioration triggered by oleic acid 222 4.2.3 Recommendations 224

Conclusions and Recommendations Aerobic Wastewater Treatment Processes 4.1



In the activated sludge study a severe bulking problem was observed during this survey which was established to be of filamentous nature. Furthermore, a relationship between the Sludge Volume Index and the filamentous bacteria contents per suspended solids could be perceived as well as a strong correlation between the Total Suspended Solids and the Total Aggregates Area.

In the protozoa and metazoa identification work the studied species attained a satisfactory overall recognition level, whereas the main protozoa and metazoa groups as well as the ciliated protozoa groups, the results were quite good. Such was also the case for the plant conditions assessment as effluent quality, aeration, sludge age, and nitrification presence. However, the assessment of critical conditions such as low effluent quality, low aeration and fresh sludge, proved to be poorer. Comparing the two multivariable statistical techniques, the overall results were lower for the Neural Networks than for the Discriminant Analysis with the exception of the critical conditions assessment.

Conclusions and Recommendations 4.1 Aerobic Wastewater Treatment Processes


4.1.1 PROTOZOA AND METAZOA IDENTIFICATION

In this study some difficulties were encountered that should be highlighted such as the complexity on the image acquisition of crawling ciliates due to their grazing abilities which promotes their partial or total incursion within the aggregates. Alongside this problem, the high mobility of some protozoa species hinders their acquisition both in terms of the protozoa microscopic survey and image quality. It must be emphasized also that some species like V. convallaria and Zoothamnium for instance, as well as the sessile in general, are morphologically speaking, quite similar on their projected form and therefore, hardly distinguishable. Moreover, a few micro-organisms like Trithigmostoma, Monogononta and Aspidisca cicada for instance, also present problems due to their body flexibility presenting themselves in different forms especially when moving or grazing which, once again, hinders their recognition.

The organism’s global recognition percentage achieved a value of 85.1% for the Discriminant Analysis and 81% for the Neural Network, which can be regarded as quite reasonable. About 1.5% of the organisms were not recognized by this procedure and 13.3% were misclassified for the Discriminant Analysis whereas for the Neural Network these values were 3% and 16% respectively. These results were not quite positive leading to a just satisfactory overall recognition level. However, the main objective of this work being the assessment of the wastewater treatment plant conditions using the protozoa and metazoa population identification and not a pure taxonomic identification of the micro-organisms, one can consider these results as a fair starting point.

Analysing the 21 studied micro-organisms, 16 shown reasonable to excellent recognition levels, whereas 5 shown a poor recognition level for the Discriminant Analysis whereas for the Neural Network those values were 14 and 7 respectively. Furthermore, 16 micro-organisms presented reasonable to excellent misclassification levels meaning that 5 presented poor misclassification levels for the Discriminant Analysis with respectively 15 and 6 micro-organisms for the Neural Network. These results can not be considered as completely positive but do not compromise this work. Moreover, an in-depth analysis shown that the micro-organisms with poorer results were mainly the stalked protozoa whereas the vast majority of the non-stalked protozoa and metazoa attained reasonable to excellent recognition and misclassification performances.

Regarding the identification of the main protozoa and metazoa groups (flagellate protozoa, ciliate protozoa, sarcodine protozoa and metazoa) as well as the ciliated protozoa groups (carnivorous, crawling, free-swimming and sessile), the results could be considered as very good. In fact, for both cases, the values of the micro-organism’s global recognition percentage was over 97%, the misclassification bellow 1.5% and about 1.5% were not identified for the Discriminant Analysis whereas for the Neural Network those values were respectively over 95%, around 2% and 2.9%. Therefore, for both cases, the results can be considered as quite good with high global recognition percentages and no significant misclassification problems.

With respect to the assessment of plant conditions such as the effluent quality, aeration, sludge age and nitrification presence assessment, the overall results proved to be quite reasonable, with global recognition percentages over 89% and misclassification errors bellow 9.1% for the Discriminant Analysis whereas for the Neural Network those values were respectively above 86% and bellow 11%. These overall results proved to be fairly good both in terms of the operating conditions assessment and the misclassification



levels. However, the assessment of critical conditions such as low effluent quality, low aeration and fresh sludge, proved to be poorer with recognition and misclassification levels just reasonable.

Furthermore, it should be noticed that the overall results were somewhat lower for the Neural Network than for the Discriminant Analysis with the exception of the critical conditions assessment more satisfactory for the Neural Networks technique.

Given the afore mentioned results multivariate statistical techniques such as Discriminant Analysis and Neural Networks proved to be a promising tool for the assessment and monitoring of protozoa and metazoa populations in a wastewater treatment plant.



4.1.2 ACTIVATED SLUDGE MONITORING

The activated sludge monitoring experiment allowed retrieving some important conclusions with respect to the relationships between the aggregates contents and morphology and the free filamentous bacteria contents on one hand and the Sludge Volume Index and the Total Suspended Solids on the other.

First of all, the very high Sludge Volume Index values, ranging from 200 mL/g to 620 mL/g throughout all the survey time, denoted the existence of a severe bulking problem. Furthermore, this situation was found to be more acute in the period between day 67 and 87 with values normally higher than 500 mL/g. Regarding the Total Suspended Solids contents they did not fell too far from the normal operating limits between 500 mg/L and 4500 mg/L, with the lower values corresponding to the period of SVI higher values.

The analysis of the aggregates global parameters did not allow to differentiate between the different types of aggregates, namely between the different size classes and, therefore, a new approach was made to the morphological data processing taking into account the different size classes throughout the experiment time. Concerning the different size classes Aggregates Number, the predominant class throughout this survey was the 0.02-0.1 mm diameter aggregates, closely followed by the 0.1-1 mm diameter class. With respect to the larger sup 1 mm aggregates no significant numbers were attained and, moreover, were almost inexistent from day 46 until the end of the survey. Moreover, the Aggregates Equivalent Diameter analysis showed a decreasing trend throughout the survey time for all the surveyed classes with the exception for the 0.1-1 mm diameter class from day 101 until the end.

The evolution of the quantity of each class of aggregates, in terms of the Aggregates Number Percentage showed a predominancy of the 0.02-0.1 mm class of aggregates throughout the survey, followed by the 0.1-1 mm aggregates, whereas the larger sup 1 mm aggregates were practically unexistant. However, as this analysis does not reflect the importance of each type of aggregate, as the Area Percentage distribution does, no further conclusions could be reached.

The Area Percentages values showed that the aggregates larger than 1 mm were not significant during the survey and practically inexistent from day 46 on, in opposition to the clear predominant 0.1-1 mm class. From the beginning of the survey time until day 12 there was a slight increase in the 0.1-1 mm class Area Percentage followed by a decrease until day 87 and a recovery from that day until the end of the survey time in a clear opposition to the behaviour of the 0.02-0.1 mm diameter class. Furthermore, the significant presence of the 0.1-1 mm diameter aggregates with respect to the 0.02-0.1 mm and to the sup 1 mm diameter aggregates seems to point out the predominance of normal flocs instead of pin point flocs or zoogleal flocs. This fact seems to point out to the conclusion that the experienced bulking problems within the aerated tank were not of zoogleal nature, but probably of filamentous nature. This hypothesis is emphasised by the time overlap of the higher Sludge Volume Index period and the smaller aggregates predominance period in the survey.

With respect to the aggregates morphological analysis the three major aspects such as the ability of the aggregates to take the lesser possible place, the elongation of the aggregate and the roughness of the aggregates edges were found to be best represented by the Solidity, Eccentricity and Convexity parameters respectively. The aggregates of the



0.02-0.1 mm and 0.1-1 mm diameter classes were found to be somewhat loose and elongated structures with smooth edges for the smaller one as opposite to the rough edges of the 0.1-1 mm diameter class. The larger aggregates presented an elongated structure and a broad range of Solidity and Convexity values, although a predominance of loose and rough edges aggregates was apparent.

With respect to the free filamentous bacteria the most significant remark was the evidence of a peak for the Filaments Length coinciding with the higher SVI values. This fact may indicate a relation amongst the liberation or growth of the filamentous bacteria and the SVI increase, although no final conclusions could be retrieved by the sole analysis of the filamentous bacteria study.

From the analysis of the Total Filaments Length per Total Suspended Solids Ratio (TL/TSS) and the Total Filaments Length per Total Aggregates Area Ratio (TL/TA) the most significant information that can be withdrawn is the fact that there was a strong peak in both parameters correlating roughly with the peak found for the Sludge Volume Index. Therefore, a straight dependence of the SVI with the relationship between the free filamentous bacteria and aggregates ratio should be considered leading towards the existence of a filamentous bulking phenomenon. Furthermore, the TL/TSS values larger than 10000 mm/mg clearly indicate the existence of a filamentous bulking problem.

Regarding the Partial Least Squares analysis in what the Sludge Volume Index is concerned, the parameter that was found to contribute the most was the TL/TA with a correlation factor of 0.8393 even after 5 points being discarded. The relationship between the PLS predicted SVI and the real SVI values obtained a correlation factor of 0.8765 after 6 points discarded, with overall differences of 17.8% when all points were considered and 9.2% when the 6 worst points were discarded. However, when plotted directly against the TL/TSS, a more satisfactory correlation factor of 0.8854 was obtained with only 3 rejected values. This fact means that the TL/TSS parameter may be used, at some extent, to monitor the SVI behaviour in a wastewater treatment plant aeration tank. In the course of this survey the Sludge Volume Index values were always quite high, ranging from 200 until 620 mL/g and consequently only the relationships for high SVI values could be studied. Therefore, for a wastewater treatment plant working with satisfactory SVI values these relations may not stand true. Furthermore, as no points were present in the lower section of the regression line the search of good correlation values may also be hindered.

With respect to the Partial Least Squares analysis for the Total Suspended Solids the parameter that was found to contribute the most was the Total Aggregates Area with a quite satisfactory correlation factor of 0.9335 for 2 points discarded. Consequently it seems realistic to infer that the TSS could be satisfactory monitored by the Total Aggregates Area. The relationship between the PLS predicted TSS and the real TSS values obtained a correlation factor of 0.9223 correlation factor with 2 points discarded with overall differences of 15.4% when all points were considered and 14.3% when the 2 worst points were discarded. Hence, and given the more satisfactory correlation factor for the relationship between the TSS and the Total Aggregates Area it was found preferable to use only this parameter to monitor the TSS contents.



4.1.3 RECOMMENDATIONS

There are a few recommendations that should be taken into account with respect to future studies on the protozoa and metazoa identification. These recommendations aim at improving the sampling technique, image acquisition and data processing methodologies and are mainly based on the difficulties encountered throughout this study.

Efforts should be made in order to restring the high mobility of some protozoa species which hinders their acquisition, namely by the addition of viscosity promoting substances to the mixed liquor.

Regarding the morphological data, new parameters should be added such as the objects signature as well as a parameter distinguishing colonial from non-colonial micro-organisms. Also, a study of the most significant parameters should be accomplished in order to determine the most important ones and promote a reduction in terms of non-significant parameters.

The number of individuals per specie for both the training and the test sets should be increased as well as the number of surveyed species, mainly for the flagellates, sarcodines and representative species of poor wastewater treatment plant operation.

In order to test the robustness of the protozoa and metazoa identification, further validation with micro-organisms from non-surveyed species should be performed.

Regarding the multivariable statistical techniques other approaches should also be taken into account in future studies such as fuzzy logic or decision trees.

Furthermore, in what the Neural Networks are concerned different layer structure should also be tested in order to determine the best Neural Network configuration.

Regarding the activated sludge monitoring a few recommendations aiming at improving both the reactor operation, sampling technique, image acquisition and data processing methodologies are in order.

For subsequent studies on the activated sludge monitoring it is advisable to also monitor aggregates smaller than the 0.02-0.1 mm diameter such as the 0.01-0.1 and inf 0.01 mm diameter as it was carried out for the anaerobic processes, by the complementary use of microscope images.

As already stressed the surveyed wastewater treatment plant was functioning, at all times, with high SVI values and a bulking phenomenon. Therefore, in future works several wastewater treatment plants should be monitored in order to allow the establishment of the relationships between the survey parameters for other conditions. These should encompass plants functioning with good operating conditions as well as other types of bulking problems or foaming problems.

With respect to the Partial Least Squares analysis apart from the linear relationships, non-linear relationships should be further investigated.



Other operating parameters like the sludge settling velocity and Volatile Suspended Solids among others should be also determined in future studies and related to the image analysis parameters.



Conclusions and Recommendations Anaerobic Wastewater Treatment Processes 4



The survey of the anaerobic granulation process allowed for the determination of an overall aggregates size and contents increase throughout the experiment as well as the establishment of the granulation time. It was also possible to identify an initial stage involving the predominant growth of the filamentous bacteria followed by a second stage of aggregates growth using the filamentous bacteria as a backbone and a final stage of balanced filamentous bacteria and aggregates contents growth. Moreover, the strong Up-Flow Velocity and Organic Loading Rate increases led to disturbances within the reactor such as the liberation of filamentous bacteria and aggregates size changes.

Concerning the granule deterioration triggered by oleic acid it was observed a biomass wash-out phenomenon throughout the experiment, a decreasing trend in the aggregates size, and an aggregate stratification with the larger aggregates in the top section of the reactor. It could also be established that this process led to more freely dispersed structures in terms of filamentous bacteria and lighter aggregates which ultimately rose to the top of the reactor, where the lighter ones suffered a wash-out phenomenon. Furthermore, it could be inferred a wash-out of the released filamentous bacteria in the reactor probably due to the selection pressure.

Conclusions and Recommendations 4 Anaerobic Wastewater Treatment Processes


4.2.1 ANAEROBIC GRANULATION PROCESS MONITORING

The anaerobic granulation process monitoring experiment allowed retrieving some important conclusions with respect to the behaviour of the aggregates contents and morphology and the free filamentous bacteria contents.

First of all, the determination of the total Area Recognition Percentage allowed for a robust method of determining the optimal dilution with regard to the image capture, processing and analysis.

The effluent Volatile Suspended Solids study points to a heavy biomass wash-out in the beginning of the experiment probably due to the selection pressure action on the lighter aggregates. Furthermore, it was also clear a strong biomass wash-out at day 115 when the biomass within the reactor was changed, and another slight wash-out with the increase of the Organic Loading Rate.

As the results of the overall Aggregates Equivalent Diameter were inconclusive, a new approach was made taking into account the different size classes and the analysis of each one separately, making possible to distinguish the behaviour of the different size classes throughout the experiment time. The Equivalent Diameter of the 0.01-0.1 mm size class increased in the beginning of the experiment until day 141 revealing a shift on the aggregates size from the range of 0.01-0.1 mm to the 0.1-1 mm diameter aggregates. The 0.1-1 mm size class presented also an increase on the Equivalent Diameter from the beginning of the experiment until day 195 with a special emphasis on the second period of operation when another shift in the aggregates size from the 0.1-1 mm to the sup 1 mm diameter range was noticeable. Finally, and yet in accordance to the suggested growth of the larger sup 1 mm aggregates from day 156 until the end of the experiment there was an effective overall increase on these aggregates Equivalent Diameter.

Concerning the Aggregates Number Percentage distribution, the predominant classes of aggregates throughout the anaerobic granulation experiment were the inf 0.01 mm followed closely by the 0.01-0.1 mm aggregates. The set of these aggregates, smaller than 0.1 mm in Equivalent Diameter, were found to be overwhelming dominant. Moreover, two key conclusions could be reached: from day 156 until the end of the second period of operation there was a decrease in the Number Percentage of the 0.1-1 mm diameter aggregates corresponding to the increase on the larger sup 1 mm aggregates and starting from day 156 the larger aggregates showed an overall increasing trend with the exception of the beginning of the third period where the Up-Flow Velocity and Organic Loading Rate were significantly increased.

The Aggregates Area Percentage, which reflects more truthfully the aggregates importance, corroborates the previous assumptions that there was a first shift of the aggregates size in the first 141 days from the range of 0.01-0.1 mm to the 0.1-1 mm diameter aggregates and a second shift in the aggregates size but this time, from the 0.1-1 mm to the 1 mm diameter range from day 156 until the end of the second period of the operation. Furthermore, in the third period of operation when the reactor was subjected to sharp increase in the Up-Flow Velocity the larger sup 1 mm aggregates Area Percentage suffered a tough decrease corresponding to a strong increase for the 0.01-0.1 mm size class in the first moment and for the 0.1-1 mm size class in a second moment. With the increase of the Organic Loading Rate, however, a noticeable shift in the Area Percentage from the 0.1-1 mm to the 0.01-0.1 mm aggregates was evident as well as an increase for the larger

Conclusions and Recommendations Anaerobic Wastewater Treatment Processes 4.2


aggregates. Finally, at the end of the third period of operation the 0.1-1 mm aggregates started to recover corresponding to a decrease in the 0.01-0.1 mm aggregates Area Percentage. These results clearly illustrate the aggregates size changes and disturbances caused by the increase of the Up-Flow Velocity and Organic Loading Rate.

Concerning the morphological parameters the three major aspects of the aggregates morphology such as the ability of the aggregate to take the lesser possible place, the elongation of the aggregate and the roughness of the aggregate edges were found to be best represented by the Solidity, Eccentricity and Convexity parameters respectively. For the larger sup 1 mm diameter class of aggregates the higher granule similitude corresponded to a time of 254 days which was found to be the most suitable value for the overall granulation time allowing the formation of smooth, compact yet elongated granular structures.

The analysis of the Total Filaments Length per Total Aggregates Area Ratio (TL/TA) and Total Filaments Length per Volatile Suspended Solids Ratio (TL/VSS) allowed to further elucidate the granulation process namely in the relationship between the filamentous bacteria and the aggregates growth. It was then possible to identify an initial stage on the granulation process until day 50 when the growth of the free filamentous bacteria contents was clearly predominant with respect to the aggregates contents. The fact that during this period the aggregates suffered some wash-out contributed to stress even more this trend. This conclusion is moreover in agreement with the behaviour of the aggregates that became more irregular and less compact. The second stage from day 50 to day 115 reflected a complete inversion of the previous trend where the aggregate contents growth largely surpassed the free filamentous bacteria growth. Once again the aggregates morphology accompanied this inversion shifting towards a more regular, compact and therefore, granule similitude. The third stage of the granulation process took place on the second period of operation and consisted on a balanced growth between the free filamentous bacteria and aggregates contents. This stage is also characterized by the lower values of the TL/TA as it could be expected for granular structures. Furthermore, at the assumed granulation time of 254 days this low filaments-to-aggregates contents ratio still stands true. Once again the increase of the Up-Flow Velocity and of the Organic Loading Rate was reflected in the behaviour of the filaments to aggregates contents ratio implying the shift of this balance towards the free filamentous bacteria.



4.2.2 GRANULE DETERIORATION TRIGGERED BY OLEIC ACID

The granule deterioration triggered by oleic acid process survey allowed a better understanding in relation to both the aggregates contents and morphology and the filaments contents behaviour. Although only samples from the top and bottom sections of the reactor were taken, an overall estimation of the different surveyed parameters was performed using the top and bottom values and their comparative numbers to interpolate the overall behaviour of the reactor.

As the sludge blanket height was found to change quite sudden and considerably with the experiment time, the absolute values of the biomass contents within the reactor could not be considered as truly faithful and therefore, were not presented. However, the results obtained for the outgoing effluent Volatile Suspended Solids reflected a biomass wash-out phenomenon throughout all the experiment time with particular emphasis after each Organic Loading Rate increase. Furthermore, the biomass adsorbed substrate (oleic acid) was significantly higher in the top section of the reactor than in the bottom section, pointing to lighter aggregates in the top section.

Analysing the total Aggregates Equivalent Diameter no solid conclusions could be withdrawn and therefore, a new approach was made taking into account the different size classes and the analysis of each one separately. As a result of that, no significant size changes were found for the smaller 0.05-0.1 mm diameter aggregates, whereas for the 0.1-1 mm diameter aggregates an overall size raise could be noticed and alongside a size reduction for the larger sup 1 mm aggregates. Although this behaviour was common to both sections and the reactor overall, they were more significant in the bottom section of the reactor, closer to the feed inlet, than in the top section of the reactor. The size increase trend of the 0.1-1 mm diameter class can be explained by the oleic acid adsorption on the aggregates making them larger, whereas the slight size decrease trend for the sup 1 mm diameter class may be the result of the larger aggregates wash-out as they become lighter. Comparing the bottom to the top section, the 0.1-1 mm diameter class presented larger values in the bottom section, opposite to the larger sup1 mm diameter aggregates presenting larger values in the top section of the reactor. Consequently, it may be inferred that the larger aggregates became lighter due to the effect of the adsorbed oleic acid and thus migrated to the top section of the reactor.

The predominant classes of aggregates in terms of the Aggregates Number Percentage were the 0.05-0.1 mm and the 0.1-1 mm aggregates, with much smaller values for the larger sup 1 mm aggregates. Furthermore, it was found an oscillating increasing trend for the 0.1-1 mm size class, in opposition to the other aggregates.

Regarding the Aggregates Area Percentage, it could be observed that the overall sup 1 mm diameter class showed a decreasing trend right from the beginning of the experiment with a sharp decrease from day 191 until the end of the experiment, in opposition to the behaviour of the overall 0.1-1 mm aggregates. These results clearly illustrate the shift of the aggregates predominant size with the increase of the Organic Loading Rate and especially from day 191, with a clear shift of the aggregates from the sup 1 mm class to the 0.1-1 mm diameter class. As the Area Percentage of the 0.1-1 mm class was found to be higher in the bottom section of the reactor and inversely the sup1 mm class in the top, it was evident an aggregate stratification within the reactor with higher contents of the larger aggregates in the top section of the reactor. This fact also strengthen

Conclusions and Recommendations Anaerobic Wastewater Treatment Processes 4.2


the previous conclusion that, with the increase on the adsorbed oleic acid, the aggregates become lighter and rose to the top of the reactor.

With respect to the aggregates morphology no really significant changes were found for the different classes in both the overall, top and bottom sections of the reactor. Nevertheless, it should be remarked that the 0.1-1 mm diameter aggregates seemed to become very slightly more compact and regular and the larger sup 1 mm diameter aggregates shown some oscillations concerning their smoothness during all the experiment time. With respect to the comparison between the top and bottom sections of the reactor no significant changes were found.

The determination of the Total Filaments Length per Total Aggregates Area Ratio (TL/TA) revealed an initial peak at day 52, followed by a correction at day 70 and an increasing trend from then until the end of the experience, mainly until day 141. Although this behaviour was common to both sections of the reactor, in the top section the TL/TA increase was significantly higher than in the bottom. It seems thus that either liberation of filamentous bacteria from the aggregates or an increased growth of the filamentous bacteria in the reactor took place with the increase of the Organic Loading Rate, with the oleic acid based substrate. Hence, a shift of the filamentous bacteria within the reactor from a well packed structure towards a more freely dispersed structure could be established. Furthermore, the comparison of the filaments-to-aggregates contents between the top and the bottom sections of the reactor showed significantly higher values for the top section. These results stress the stratification of the relationship between the free filamentous bacteria and the aggregates, mainly from day 92 on, with the oleic acid based substrate, with much higher values in the top section of the reactor. In conclusion the granule deterioration process by the increase on the adsorbed oleic acid, led to lighter aggregates, with more freely dispersed structure in terms of the free filamentous bacteria which ultimately rose to the top of the reactor, where the lighter ones suffered a wash-out phenomenon.

An interesting analysis in the granulation studies is the determination of the Fines and Non-Fines fractions. Regarding the overall Fines Area Percentage it was noticeable a slight wash-out of the smaller aggregates until day 70, and from then until the end of the experiment a continuous increase. The overall Fines Weight Percentage demonstrated a similar behaviour to the Fines Area Percentage with the exception of the last day where it suffered a slight decrease leading to a satisfactory correlation, although not quite conclusive, between these parameters. Furthermore, the correlations between the TL/TA ratio and the Fines Area Percentage for the top and bottom sections of the reactor allowed to establish the dependence of the filamentous bacteria contents with the aggregates fragmentation up to a value of 40% of Fines. From that point on the filament contents assumed constant values which could be explained by the selection pressure leading to a wash-out of the non-attached filaments. Nonetheless, further studies should be made in order to further enlighten this relationship.



4.2.3 RECOMMENDATIONS

There are a few recommendations that should be taken into account with respect to future studies on this subject. These recommendations aim at improving both the reactor operation, sampling technique, image acquisition and data processing methodologies and are mainly based on the difficulties encountered throughout this study.

A more reliable method of determining the aggregates and filaments contents within the reactor for the granule deterioration study should be sought in order to monitor most effectively the biomass wash-out.

The aggregates morphology and contents and the filaments contents leaving the reactor should also be monitored in order to establish the true nature of the wash-out biomass.

With respect to the granule sampling and image acquisition a new methodology should be put in practice based on the full scrutiny of a constant given volume retrieved directly from the reactor. This is due to the propensity of the aggregates to deposit when the dilutions are performed and therefore, the sampling technique is not optimal and can leave to the granules overestimation.

For subsequent studies on the granule deterioration process it is advisable to also monitor aggregates smaller than the 0.05-0.1 mm diameter such as the 0.01-0.1 and inf 0.01 mm diameter as it was carried out for the granulation process, by the complementary use of microscope images.

All the different size classes should be monitored from the beginning including the larger ones though scarce or non-existing in certain periods of the survey.

The filaments survey should also take notice and differentiate between the filamentous bacteria effectively attached to the aggregates and the freely dispersed filamentous bacteria in order to establish the filaments liberation from the filaments growth process.

Concerning the reactor operation it would be advisable to make more changes throughout the time of operation and not quite so severe with the aim of softening the abrupt variations in the aggregates and filaments contents and morphology and therefore, obtain a more stable and reliable behaviour of this parameters.

With the same purpose it would also be preferred to collect the samples at the end of each operating change in the reactor when a more stabilized state is achieved.

The dependence of the filamentous bacteria contents with the aggregates fragmentation should be further studied should in order to allow a robust clarification of this relationship.

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Author Index


AUTHOR INDEX Autores

Adenwala ............................................................................................................................... 34, 229

Ahn.......................................................................................................................................... 35, 225

Ahring ............................................................................................................................... 65, 66, 233

Alves...............................................................................................35, 65, 76, 79, 225, 226, 227, 232

Al-Yousfi................................................................................................................................. 50, 225

Amaral .......................................................................................34, 35, 52, 59, 67, 83, 225, 226, 227

APHA................................................................................................................................ 82, 83, 226

Araya-Kroff .......................................................................................35, 71, 76, 77, 78, 81, 176, 226

Avery..................................................................................................................................... 126, 234

Balmer ..................................................................................................................................... 50, 234

Baptiste...................................................................................................................... 35, 59, 225, 226

Barbusinski............................................................................................................................. 50, 226

Batstone................................................................................................................................... 66, 226

Beale ...................................................................................................................................... 140, 228

Bellouti .............................................................................................................................. 35, 67, 226

Bitton ........................................................................................42, 44, 46, 47, 48, 50, 52, 62, 64, 226

Brouzes.................................................................................................................................... 44, 226

Bryant...................................................................................................................................... 63, 231

Burton ....................................................................................................... 42, 43, 44, 47, 82, 83, 233

Canler................................................................................. 46, 47, 48, 53, 54, 55, 56, 58, 151, 226, i

Cenens....................................................................................................................... 35, 38, 226, 227

Chamy............................................................................................................................... 35, 67, 230

Chao ........................................................................................................................................ 50, 227

Cotteux............................................................................................................................ 51, 226, 228

Crawford ................................................................................................................................ 34, 232

Curds................................................................................................................................. 53, 57, 227

da Motta...........................................................................................35, 52, 59, 71, 75, 225, 226, 227

Daffonchio .............................................................................................................................. 67, 228

David....................................................................................................................................... 34, 228

de Man .................................................................................................................................... 76, 234

de Zeeuw ................................................................................................................ 66, 228, 230, 231

Author Index


Demuth ................................................................................................................................. 140, 228

Desroches................................................................................................................................ 34, 228

Dolfing .................................................................................................................................... 66, 228

Dougherty............................................................................................................................... 37, 228

Dubourguier........................................................................................................................... 66, 228

Duchène .................................................................................................................................. 51, 228

Dudley............................................................................................................................... 35, 67, 228

Eikelboom............................................................................................................................... 48, 228

Einax................................................................................................................ 59, 136, 138, 139, 228

Erikson .................................................................................................................................... 49, 228

Fallowfield.............................................................................................................................. 34, 231

Fang ......................................................................................................................................... 66, 228

Fenchel .................................................................................................................................... 55, 228

Finlay.................................................................................................................................... 53, 228, i

Forster ............................................................................................................................... 46, 47, 228

Fowler ..................................................................................................................................... 34, 228

Ganczarczyk..................................................................................................................... 35, 52, 229

Gerardi .................................................................................................................................... 50, 229

Glasbey.................................................................................. 34, 38, 39, 97, 106, 126, 128, 130, 229

Golz ................................................................................................................................... 35, 59, 229

Gonzalez ................................................................................................... 86, 87, 88, 89, 91, 98, 229

Gregory ................................................................................................................................... 50, 229

Grijspeerdt........................................................................................................................ 35, 52, 229

Gujer .................................................................................................................................. 62, 63, 229

Hammonds............................................................................................................................. 34, 229

Hanaki..................................................................................................................................... 67, 229

Hanel ....................................................................................................................................... 50, 229

Härdin ..................................................................................................................................... 49, 228

Heine ....................................................................................................................................... 35, 229

Hermanowicz................................................................................................................. 67, 132, 229

Horgan .................................................................................. 34, 38, 39, 97, 106, 126, 128, 130, 229

Howgrave-Graham ................................................................................................. 35, 67, 228, 229

Huang.................................................................................................................................... 132, 230

Hulshoff Pol ..................................................................................................................... 65, 66, 230

Huser ....................................................................................................................................... 63, 230

Author Index


Hwu......................................................................................................................................... 67, 230

Ison .......................................................................................................................................... 34, 234

Jahn............................................................................................................. 47, 55, 57, 58, 151, 230, i

Jähne.................................................................................................................................. 37, 38, 230

Jeison ................................................................................................................................. 35, 67, 230

Jenkins............................................................................................................................... 49, 52, 230

Jewell ....................................................................................................................................... 64, 231

Jorand.............................................................................................................................. 49, 230, 233

Kasabov ...........................................................................................59, 140, 141, 142, 143, 144, 230

Keinath.................................................................................................................................... 50, 227

Koscielniak ............................................................................................................................. 50, 226

Koster ...................................................................................................................................... 62, 230

Kumaran ................................................................................................................................. 42, 230

Lawrence .................................................................................................................. 42, 64, 230, 231

Lee ..................................................................................................................................... 38, 52, 225

Leondes................................................................................................................................... 59, 231

Lester................................................................................................................................. 43, 46, 233

Lettinga ............................................................................................................... 43, 64, 65, 230, 231

Liebovitch ............................................................................................................................. 132, 231

Mackie..................................................................................................................................... 63, 231

Madoni............................................................................... 48, 49, 53, 54, 55, 56, 57, 58, 151, 231, i

Mah.......................................................................................................................................... 63, 233

Maltin ...................................................................................................................................... 34, 231

Mandelbrot................................................................................................................................... 132

Martin...................................................................................................................................... 34, 231

McCarty .......................................................................................................................... 42, 230, 233

McFarland .............................................................................................................................. 64, 231

Morrin ..................................................................................................................................... 34, 231

Nelson ..................................................................................................................................... 42, 231

Neves............................................................................................................. 71, 77, 78, 81, 226, 231

Nicolau.............................................................................................................. 54, 55, 225, 226, 231

Noesis.......................................................................................74, 75, 86, 88, 91, 126, 127, 128, 232

Novak...................................................................................................................................... 50, 232

Obert.............................................................................................................................................. 133

Otsu ......................................................................................................................................... 38, 232

Author Index


Palm......................................................................................................................................... 52, 232

Patankar .................................................................................................................................. 34, 232

Paul.......................................................................................................................................... 34, 228

Pereira ........................................................................................... 71, 79, 80, 81, 193, 225, 226, 232

Polprasert.......................................................................................................................... 62, 64, 232

Pons ..............................................................34, 37, 89, 116, 128, 129, 134, 225, 226, 227, 228, 232

Pujol......................................................................................................................................... 48, 232

Richard.................................................................................................... 47, 49, 53, 55, 57, 230, 232

Rinzema .................................................................................................................................. 67, 232

Ritz........................................................................................................................................... 34, 232

Rodrigues.............................................................................................. 71, 72, 73, 81, 158, 226, 232

Russ ............................................................................................. 38, 87, 89, 125, 126, 130, 132, 232

Russel ...................................................................................................................................... 34, 233

Sahm.................................................................................................................................. 43, 64, 233

Sawyer..................................................................................................................................... 42, 233

Schink................................................................................................................................ 62, 64, 233

Schmidt ............................................................................................................................. 65, 66, 233

Seviour ............................................................................................................................ 52, 134, 233

Sezgin ........................................................................................................................ 50, 52, 165, 233

Shivaraman............................................................................................................................. 42, 230

Singh.................................................................................................................................. 35, 68, 233

Smith ....................................................................................................................................... 63, 233

Snidaro .................................................................................................................................... 49, 233

Soddell ............................................................................................................................ 52, 134, 233

Speece................................................................................................................................ 62, 63, 233

Sterrit ................................................................................................................................. 43, 46, 233

Switzenbaum.......................................................................................................................... 43, 233

Sykes.................................................................................................................................. 48, 49, 233

Tchobanoglous......................................................................................... 42, 43, 44, 47, 82, 83, 233

Thauer ..................................................................................................................................... 62, 233

The Mathworks, Inc .............................................................................. 72, 73, 77, 78, 80, 127, 231

Toth........................................................................................................................................ 132, 231

Unz .......................................................................................................................................... 48, 234

Vecht-Lifschitz ....................................................................................................................... 34, 234

Verstraete.................................................................................................................. 35, 52, 228, 229

Author Index


Vivier............................................................................34, 37, 89, 116, 128, 134, 225, 226, 227, 232

Wallis................................................................................................................................. 35, 67, 228

Walsby .................................................................................................................................. 126, 234

Wanner.................................................................................................................................... 48, 234

Ward........................................................................................................................................ 34, 231

Westlund ................................................................................................................................ 48, 234

Wiegant....................................................................................................................... 65, 66, 76, 234

Wilen ....................................................................................................................................... 50, 234

Williams.................................................................................................................................. 48, 234

Woods ....................................................................................................... 86, 87, 88, 89, 91, 98, 229

Wu ..................................................................................................................................... 50, 66, 234

Zehnder............................................................................................................. 62, 63, 229, 230, 233

Zeikus...................................................................................................................................... 62, 234

Author Index


Appendix A: Short protozoa and metazoa guide

António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment i

APPENDIX A: SHORT PROTOZOA AND METAZOA GUIDE

In this section a brief guide to the most common protozoa and metazoa found in activated sludge is provided. This characterization is based on the descriptions found in Canler et al., 1999; Finlay et al., 1988; Madoni, 1994 and Jahn et al. 1994. The pictures of the protozoa and the metazoa are some examples of the images used in this work. The black bar in each figure represents 10 µm.

Protozoa Flagellates

Peranema sp.: 20-100 µm. Elongated and deformable cylindrical body with rounded posterior extremity. Oral aperture in front position. Robust flagellum in front position and second flagellum hardly distinguishable. Forward motion in the flagellum direction. Feed: bacteria. Present in low loads and good effluent quality.

Sarcodines

Arcella sp.: Testate amoeba. 30-250 µm. Rigid test, circular or ovoid view from the top and bowl-shaped from lateral view. Central test aperture from where the pseudopodium can exit. Present in low loads or prolonged aeration, good nitrification efficiency and effluent quality.

Euglypha sp.: Testate amoeba. 50-150 µm. Ovoid or laterally compressed test made by well visible ovoid plaques. Test aperture in the narrowest extremity. Feed: bacteria. Present in low loads or prolonged aeration, good nitrification efficiency and effluent quality.


António Luís Pereira do Amaral ii Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Ciliates:

Trachelophyllum sp.: Holotrichia: 40-50 µm. Bottle-shaped body elongated and very flexible. Oral aperture in front position. A single terminal vacuole and uniformly ciliated. Feed: bacteria and flagellates. Present in all loads, indicator of transient phenomena and mediocre effluent quality.

Litonotus sp.: Holotrichia: 50-200 µm. Elongated body, laterally compressed and flexible. Contractile terminal vacuole. Larger cilia around the front oral aperture. Feed: bacteria, flagellates and ciliates. Present in medium loads, indicator of transient phenomena.

Trithigmostoma sp.: Holotrichia: 40-300 µm. Asymmetric body with a dorsal bump. Scar-shaped oral aperture well distinguishable in front position. Vacuoles in posterior position. Feed: bacteria and other protozoa. Present in all loads and good effluent quality.


António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment iii

Trochilia sp.: Holotrichia: 50 µm. Dorso-ventrally flattened body with a tube-shaped oral aperture and high mobility. Feed: bacteria. Present in low loads. Good nitrification efficiency and effluent quality.

Aspidisca cicada sp.: Spirotrichia – Hipotrichia: 25-45 µm. Ovoid, dorso-ventrally flattened body with dorsal ridges, high mobility, nourishing on the flocs and with 5 cirri in posterior position. Feed: bacteria. Present in all loads and stable plant conditions.

Euplotes sp.: Spirotrichia – Hipotrichia: 50-200 µm. Ovoid to ellipsoid body, elongated and dorso-ventrally flattened, with dorsal ridges. Moves over the flocs with cirri in the oral aperture and 5 to 7 cirri in posterior position. Feed: bacteria and small flagellates. Present in medium to low loads, stable plant conditions, reasonable nitrification efficiency and good effluent quality.

Opercularia sp.: Peritrichia: 40-120 µm. Colonial, body shaped as an elongated vase, with a cilia crown in the oral aperture. Contractile vacuole and C-shaped macronucleus, large non-retractile stalk without myoneme. Feed: bacteria. Present in high loads, resistant to certain toxics, acid pH and low aeration. Indicator of a mediocre effluent quality.


António Luís Pereira do Amaral iv Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Epistylis sp.: Peritrichia: 70-100 µm. Colonial, body shaped as an elongated vase, with a cilia crown in the oral aperture. Contractile vacuole and C-shaped macronucleus, large non-retractile stalk without myoneme. Feed: bacteria. Present in low loads, stable conditions and good effluent quality.

V. convallaria: Peritrichia: 30-120 µm. Bell shaped body with a cilia crown in the oral aperture. Contractile stalk with myoneme and C-shaped macronucleus. Feed: bacteria. Present in medium to high loads.

V. microstoma: Peritrichia: 20-60 µm. Ovoid body slightly striated with a cilia crown in the narrow oral aperture. Contractile stalk with myoneme and elongated macronucleus, and a vacuole. Feed: bacteria. Present in high loads and low aeration. Indicator of a mediocre effluent quality.


António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment v

V. aquadulcis: Peritrichia: 20-50 µm. Ovoid body slightly elongated very striated with a cilia crown in the narrow oral aperture. Contractile stalk with myoneme and transverse macronucleus, and a vacuole. Feed: bacteria. Present in medium to low loads, good aeration and effluent quality.

Zoothamnium sp.: Peritrichia: 80 µm. Colonial, bell shaped body, with a cilia crown in the large oral aperture. Stalk with myoneme and simultaneously contractile for all the individuals. Elongated macronucleus, and with vacuole. Feed: bacteria. Present in low loads, stable conditions, good aeration and effluent quality.

Carchesium sp.: Peritrichia: 80-140 µm. Colonial, distorted bell shaped body, with a cilia crown in the oral aperture. Large stalk with myoneme and independently contractile for all the individuals. Horse shoe shaped macronucleus, and with vacuole. Feed: bacteria. Present in medium loads, stable conditions, good aeration and effluent quality.

Tokophrya sp.: Suctoria: 50-70 µm. Body shaped as an inverted pyramid, myoneme-less narrow stalk and tentacles disposed in four distinct tufts. Hollow tentacles used for feeding purposes. Feed: swimming protozoa. Present in low loads.


António Luís Pereira do Amaral vi Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Acineta sp.: Suctoria: 30-300 µm. Body shaped as an inverted pyramid, myoneme-less stalk and tentacles disposed in two tufts. Cytoplasm does not entirely fulfil the body. Hollow tentacles used for feeding purposes. Feed: swimming protozoa. Present in low loads.

Podophrya sp.: Suctoria: 10-60 µm. Spherical body, myoneme-less stalk and tentacles all over the body. Hollow tentacles used for feeding purposes. Feed: swimming protozoa.

Metazoa Rotifers

Digononta: 100-250 µm. Telescopic body, narrower posterior extremity and composed by several segments. Two cilia crowns in the oral aperture, mobile and attaching to the flocs by the posterior end. Feed: bacteria. Present in low loads.

Monogononta: 50-300 µm. Clear distinction between the head, body and foot, dorso-ventrally flattened with a rigid shell and a one or two finger foot and mobile. Feed: bacteria. Present in low loads, stable conditions, good nitrification efficiency and effluent quality.


António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment vii

Gastrotrichia

Nematoda: >150 µm. Very narrow body, flat and mobile. Feed: Debris and other protozoa. Present in all loads and resistant to low aeration.

Oligotrichia

Aelosoma sp.: >500 µm. Rounded front extremity with tufts across the body, segmented in rings and mobile. Feed: Debris. Present in very low loads, stable conditions, good aeration, nitrification efficiency and effluent quality.


António Luís Pereira do Amaral viii Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Appendix B: Calibration of the morphological parameters

António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment ix

APPENDIX B: CALIBRATION OF THE MORPHOLOGICAL PARAMETERS

In order to confirm the accuracy of the morphological parameters determined within the programmes, a few common shapes with known values were used. Three different sets of shapes were used to study different aspects of the morphology. For each of these shapes five different sizes were studied in order to establish the parameters dependence on the shapes size.

The first set, shown in Figure I, allowed for the morphological parameters distinction as it studied the three major aspects of the aggregates morphology: the ability of the aggregates to take the lesser possible space, the elongation of the aggregate and the roughness of the aggregates edges.

The second set, shown in Figure II, allowed for the morphological parameters calibration, as it represented shapes with known values.

The third set, shown in Figure III, allowed for the determination of the different fractal dimensions fitness, as it represented shapes with increasing fractal morphology.

Figure I – Objects used for the morphological parameters distinction.

Circ Elip Plus Star


António Luís Pereira do Amaral x Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Figure II – Objects used in the morphological parameters calibration.

Figure III – Objects used in the fractal dimensions fitness.

The results for the morphological parameters distinction for each of the shapes in Figure I are presented in Figure IV. The five different sizes are shown from left to right representing respectively the larger to the smaller sizes. The A

PD , AFDD and P

FDD fractal dimensions were not determined for each size separately but for the whole set of sizes and therefore, there is only one value for all the different sizes of each shape as they formed the set of objects.

Analysing the differences between the results for each shape and size, the ability of the aggregates to take the lesser possible space was found to be better represented by the

Sqr Rec Tri

Frc1 Frc2 Frc4 Frc5 Frc3


António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xi

Solidity and Robustness parameters, the elongation of the aggregate by the Compactness and Eccentricity parameters and the roughness of the aggregates edges by the Convexity, A

PD and P

FDD parameters.

1

1.5

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Figure IV – Results obtained for the morphological parameters distinction.


António Luís Pereira do Amaral xii Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

(continuation of Figure IV)

0

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António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xiii

(continuation of Figure IV)

1

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Circ

DM

R

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Elip Plus Star

Elip Plus Star

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The results for the morphological parameters calibration for each of the shapes in Figure II are presented in Figure V. The five different sizes are shown from left to right


António Luís Pereira do Amaral xiv Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

representing respectively the larger to the smaller sizes. Analysing these results it was found that the DBM, DEDM and DMR parameters were quite dependent on the aggregates size and therefore, not very robust.

1

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Sqr

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Figure V – Results obtained for the morphological parameters calibration.


António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xv

(continuation of Figure V)

0

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Rect Tri Circ

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António Luís Pereira do Amaral xvi Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

(continuation of Figure V)

1

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Sqr

DM

R

Rect Tri Circ

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Rect Tri Circ

Rect Tri Circ

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António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xvii

The results for the fractal dimensions fitness for each of the shapes in Figure III are presented in Figure VI. The five different sizes are shown from left to right representing respectively the larger to the smaller sizes. Analysing these results it was found that the shape factor, DBM, DBS, DEDM and DMR parameters were quite dependent on the aggregates size and therefore, not very robust. Furthermore, the Compactness parameter was found to be dependent on characteristics other than the elongation and hence, not suitable for the elongation determination.

It should be remarked though that the APD , A

FDD and PFDD parameters are quite

dependent on the number of objects on one hand and can only be taken into account for high regression factors.

0

10

20

30

40

50

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ctor


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ity


Figure VI – Results obtained for the fractal dimensions fitness.


António Luís Pereira do Amaral xviii Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

(continuation of Figure VI)

0

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António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xix

(continuation of Figure VI)

1

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R


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Frc1 Frc2 Frc3 Frc5Frc4



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António Luís Pereira do Amaral xx Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Appendix C: CD contents

António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xxi

APPENDIX C: CD CONTENTS Due to the impracticality of referring in this hard copy all the desired information

in the body of this thesis, a CD is also presented as part of the thesis. The contents of the aforementioned CD are divided into folders treating different subjects as follows:

Protozoa and metazoa morphological data → The 36 morphological

parameters of each of the 21 protozoa and metazoa are presented in the Protozoa and Metazoa acrobat file within the Morphological Parameters folder.

Activated sludge morphological data → The 7 morphological parameters for each day of the activated sludge survey are presented in the Activated sludge monitoring acrobat file within the Morphological Parameters folder.

Granulation process morphological data → The 21 morphological parameters for each day of the granulation process experiment are presented in the Anaerobic granulation process acrobat file within the Morphological Parameters folder.

Granule deterioration morphological data → The 21 morphological parameters for each day of the granule deterioration experiment are presented in the Granule deterioration triggered by oleic acid acrobat file within the Morphological Parameters folder.

Protozoa and metazoa Visilog programme → The developed Visilog programme for the determination of the protozoa and metazoa morphological parameters is presented in the ProtoRec acrobat file within the Programmes folder.

Granules Matlab programme → The developed Matlab programme for the determination of the granules morphological parameters is presented in the Granules acrobat file within the Programmes folder.

Flocs Matlab programme → The developed Matlab programme for the determination of the flocs morphological parameters is presented in the Flocs acrobat file within the Programmes folder.

Filaments Matlab programme → The developed Matlab programme for the determination of the filaments contents is presented in the Filaments acrobat file within the Programmes folder.

Appendix C: CD contents

António Luís Pereira do Amaral xxii Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Appendix D: Publication List

António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xxiii

APPENDIX D: PUBLICATION LIST International Journals and Book Chapters

1. Automated Image Analysis to Improve Bead Ingestion Toxicity Test Counts in the Protozoan Tetrahymena pyriformis (Dias, N., Amaral, A.L., Ferreira, E.C., Lima, N.) – Letters in Applied Microbiology, 37 (in press).

2. Morphological Analysis of Yarrowia lipolytica under Stress Conditions through Image Processing (Kawasse, F.M., Amaral, P.F., Rocha-Leão, M.H., Amaral, A.L., Ferreira, E.C., Coelho, M.A.) – Bioprocess and Biosystems Engineering (in press).

3. Integrated Approach Combining Image Analysis, Methanogenic Activity and Molecular Ecology Techniques to Monitor Granular Sludge from an EGSB Reactor Fed with Oleic Acid (Pereira, A., Roest, K., Stams, A.J.M., Akkermans, A.D.L., Amaral, A.L., Pons, M.N., Ferreira, E.C., Mota, M., Alves, M.M.) – Water Science & Technology, 47: 5: 181-188, 2003.

4. Characterisation of Activated Sludge by Automated Image Analysis: Validation on Full-Scale Plants (da Motta, M., Amaral, A.L., Casellas, M., Pons, M.N., Dagot, C., Roche, N., Ferreira, E.C., Vivier, H.) – Computer Applications in Biotechnology 2001, (Dochain, D., Perrier, M. Eds.), Pergamon Press, Oxford, 427-431, 2002.

5. Reconaissance Semi-Automatique de la Microfaune des Boues Activées des Stations de Traitment d’Eaux Usées: ProtoRec v2.0 (da Motta, M., Pons, M.N., Vivier, H., Roche, N., Amaral, A.L., Ferreira, E.C., Mota, M.) – Récents Progrés en Génie des Procédés, 15: 78: 167-172, 2001.

6. Study of Protozoa Population in Wastewater Treatment Plants by Image Analysis (da Motta, M., Pons, M.N., Vivier, H., Amaral, A.L., Ferreira, E.C., Mota, M.) – Brazilian Journal of Chemical Engineering, 18: 1: 103-111, 2001.

7. Characterisation by Image Analysis of Anaerobic Sludge under Shock Conditions (Alves, M.M., Cavaleiro, A.J., Ferreira, E.C., Amaral, A.L., Mota, M., da Motta, M., Vivier, H., Pons, M.N.) – Water Science & Technology, 41: 12: 207-214, 2000.

8. Staged and Non-Staged Anaerobic Filters: Performance in Relation with the Physical and Biological Characteristics of Microbial Aggregates (Alves, M.M., Ferreira, E.C., Amaral, A.L., Pereira, A., Novais, J.M., Mota, M.) – Journal of Chemical Technology and Biotechnology, 75: 7: 601-609, 2000.

9. Semi-Automated Recognition of Protozoa by Image Analysis (Amaral, A.L., Baptiste, C., Pons, M.N., Nicolau, A., Ferreira, E.C., Mota, M., Vivier, H.) – Biotechnology Techniques, 13: 2: 111-118, 1999.

National Journals

10. Estudo por Análise de Imagem do Comportamento de uma Estação de Tratamento de Efluentes sob Condições Transientes (da Motta, M., Amaral, A.L., Pons, M.N., Ferreira, E.C., Vivier, H., Mota, M.) – Energias Renovables y Medio Ambiente, 9: 49-55, 2001.

11. Aplicação de um Sistema de Análise de Imagem à Monitorização da Fermentação (Costa, A.M., Vicente, A., Amaral, A.L., Ferreira, E.C., Teixeira, J.A., Cruz, J.M.) – Cerveja, 18: 27, 2001.


António Luís Pereira do Amaral xxiv Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Conference Proceedings (full articles)

12. Efeito da Diluição na Caracterização da Biomassa de Sistemas de Tratamentos de Efluentes por Análise de Imagem (Da Motta, M., Amaral, A.L., Neves, L., Araya-Kroff, P., Ferreira, E.C., Alves, M.M., Mota, M., Roche, N., Vivier, H., Pons, M.N.) – Proceedings of the 14th Brazilian Congress of Chemical Engineering, Natal, CD-ROM, 2002.

13. Image Analysis as a Tool to Recognize Anaerobic Granulation Time (Araya-Kroff, P., Amaral, A.L., Neves, L., Ferreira, E.C., Alves, M. M., Mota M.) – Proceedings of the 7th Latin American Worksop and Symposium in Anaerobic Digestion, Mérida, 31-38, 2002.

14. Morphological Characterisation of Biosolids in Wastewater Treatment using Partial Least Squares (Amaral, A.L., Rodrigues, S., Mota, M., Ferreira, E.C.) – Proceedings of the 2nd IASTED International Conference on Visualization, Image and Image Processing, Málaga, 300-305, 2002.

15. Image Analysis, Methanogenic Activity and Molecular Biological Techniques to Monitor Granular Sludge from an EGSB Reactor Fed with Oleic Acid (Pereira, A., Roest, K., Stams, A.J.M., Akkermans, A.D.L., Amaral, A.L., Pons, M.N., Ferreira, E.C., Mota, M., Alves, M.M.) – Proceedings of the International Specialized Conference in Biofilm Monitoring, Porto, 343-346, 2002.

16. Survey of a Wastewater Treatment Plant Microfauna by Image Analysis (Amaral, A.L., da Motta, M., Pons, M.N., Vivier, H., Mota, M., Ferreira, E.C.) – Proceedings of the 8th International Conference on Chemical Engineering, Aveiro, 2: 1137-1144, 2001.

17. Characterisation by Image Analysis of Anaerobic Sludge from Two EGSB Reactors Treating Oleic Acid: Automatic Detection of Granules Disintegration by Image Analysis (Amaral, A.L., Pereira, M.A., Neves, L., da Motta, M., Pons, M.-N., Vivier H., Mota, M., Ferreira, E.C., Alves, M.M.) – Proceedings of the 9th World Congress on Anaerobic Digestion, Antuérpia, 1: 89-94, 2001.

18. Characterisation of Activated Sludge by Automated Image Analysis: Validation on Full Scale Plants (da Motta ,M., Amaral, A.L., Casellas, M., Pons, M.N., Dagot, C., Roche, N., Ferreira, E.C., Vivier, H.) – Proceedings of the 8th International Conference in Computer Application on Biotechnology, Québec City, 452-456, 2001.

19. Study of Protozoa Population in Wastewater Treatment Plants by Image Analysis (da Motta, M., Pons, M.N., Vivier, H., Amaral, A.L., Ferreira, E.C., Mota, M.) – Proceedings of the 19th Chemical Engineering Inter-American Congress and 14th Brazilian Congress of Chemical Engineering, Águas de S. Pedro, 24-27, 2000.

20. Characterisation by Image Analysis of Anaerobic Sludge Under Shock Conditions (Alves, M.M., Cavaleiro, A.J., Ferreira, E.C., Amaral, A.L., Mota, M., da Motta, M., Vivier, H., Pons, M.N.) – Proceedings of the 4th International Symposium on Environmental Biotechnology, Noordwijkerhout, 127-130, 2000.

21. Automated Monitoring of Activated Sludge using Image Analysis (da Motta, M., Pons, M.N., Roche, N., Amaral, A.L., Ferreira, E.C., Alves, M.M., Mota, M., Vivier, H.) – Proceedings of the 1st Water World Congress of the IWA, Paris, CD-ROM, 2000.

22. Aplicações de Técnicas de Análise de Imagem em Microbiologia Ambiental (Amaral, A.L., Mota, M., Ferreira, E.C.) – Proceedings of the 6th National Conference on the Environment Quality, Lisboa, 2: 29-38, 1999.

23. Analyse des Flocs Bacteériens et de la Microfaune des Boues Activées par Analyse d’Image (da Motta, M., Pons, M.N., Roche, N., Amaral, A.L., Ferreira, E.C., Mota, M.) –


António Luís Pereira do Amaral Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment xxv

Proceedings of the 3rd International Congress ‘L’Eau et sa Réutilization’, Toulouse, 321-326, 1999.

24. Morphological Characterisation of Microbial Aggregates by Image Analysis (Amaral, A.L., Alves, M.M., Mota, M., Ferreira, E.C.) – Proceedings of the 9th Confererence on Pattern Recognition, Coimbra, 95-100, 1997.

Conference Proceedings (abstracts)

25. Classification of Saccharomyces cerevisiae Morphology using Image Analysis (Coelho, M.A., Amaral, A.L., Belo, I., Mota, M., Coutinho, J., Ferreira, E.C.) – Proceedings of the 4th European Symposium in Biochemical Engineering Sciences, Delft, 98, 2002.

26. Morphological Analysis of Yarrowia lipolytica under Stress Conditions through Image Processing (Kawasse, F.M., Amaral, P.F., Rocha-Leão, M.H.M., Coelho, M.A., Amaral, A.L., Ferreira, E.C.) – Proceedings of the 4th European Symposium in Biochemical Engineering Sciences, Delft, 97, 2002.

27. Bulking Filamentoso na ETAR de Braga – Análise Diagnóstico e Soluções (Rodrigues, S., Amaral, A.L., Amorim, C., Pereira, R., Ferreira, E.C.) – Proceedings of the 10º Encontro Nacional de Saneamento Básico, Braga, CD-ROM, 2002.

28. Characterisation of Bubbles in a Bubble Column by Image Analysis (Freitas, C., Amaral, A.L., Fialova, Ferreira, E.C., Zahradnik, J., Teixeira, J.A.) – Proceedings of the 14th International Congress on Process and Chemical Engineering, Prague, 27-31, 2000.

29. Automatic Determination of Yeast Cells Viability by Image Analysis (Pinheiro, R., Amaral, A.L., Ferreira, E.F. Mota, M.) – Proceedings of the 4th Iberian Congress on Biotechnology and 1st Ibero-American Meeting on Biotechnology, (Mota, M., Ferreira, E.C, Eds), Guimarães, 262, 1998.

30. Mobility Assessment of the Ciliated Tetrahymena pyriformis after Exposition to Toxic Compounds using Image Analysis (Amaral, A.L., Nicolau, A., Ferreira, A.C., Lima, N., Mota, M.) – Proceedings of the 4th Iberian Congress on Biotechnology and 1st Ibero-American Meeting on Biotechnology, (Mota, M., Ferreira, E.C, Eds), Guimarães, 322, 1998.

31. Monitoring Methanogenic Fluorescence by Image Analysis (Amaral, A.L., Alves, M,M., Mota, M., Ferreira, E.C.) - Proceedings of the 4th Iberian Congress on Biotechnology and 1st Ibero-American Meeting on Biotechnology, (Mota, M., Ferreira, E.C, Eds), Guimarães, 365, 1998.

32. Automated Characterisation of Protozoa in Activated Sludge (Baptiste, C., Amaral, A.L., Nicolau, A., Pons, M.N., Lima, N., Ferreira, E.C., Mota, M., Vivier, H.) – Proceedings of the 2nd European Symposium on Biochemical Engineering Sciences, Porto, 339, 1998.

Thesis

33. Desenvolvimento de Técnicas de Análise de Imagem para Aplicação em Processos Biotecnológicos (Amaral, A.L.) – Msc. thesis in Biological Engineering, Universidade do Minho, Braga, 1996.


António Luís Pereira do Amaral xxvi Image Analysis in Biotechnological Processes: Applications to Wastewater Treatment

Documents

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