BIOWINTM MODELING OF THE STARTUP OF AN …ourspace.uregina.ca/bitstream/handle/10294/5487/Yang_Wenwen... · the Startup of an Anaerobic Digester Used in Wastewater Treatment Plants

BIOWINTM

MODELING OF THE STARTUP OF AN ANAEROBIC DIGESTER

USED IN WASTEWATER TREATMENT PLANTS

A Thesis

Submitted to the Faculty of Graduate Studies and Research

In Partial Fulfillment of the Requirements

For the Degree of

Master of Applied Science

in

Environment Systems Engineering

University of Regina

By

Wenwen Yang

Regina, Saskatchewan

March, 2014

Copyright 2014: Wenwen Yang

UNIVERSITY OF REGINA

FACULTY OF GRADUATE STUDIES AND RESEARCH

SUPERVISORY AND EXAMINING COMMITTEE

Miss Wenwen Yang, candidate for the degree of Master of Applied Science in Environmental Systems Engineering, has presented a thesis titled, BIOWINTM Modeling of the Startup of an Anaerobic Digester Used in Wastewater Treatment Plants, in an oral examination held on March 14, 2014. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material. External Examiner: Mr. Sean Bayer, KGS Group Consulting Engineering

Co-Supervisor: Dr. Stephanie Young, Environmental Systems Engineering

Co-Supervisor: Dr. Christine Chan, Software Systems Engineering

Committee Member: *Dr. Guo H. Huang, Environmental Systems Engineering

Committee Member: Dr. Liming Dai, Industrial Systems Engineering

Committee Member: Dr. Ezeddin Shirif, Petroleum Systems Engineering

Chair of Defense: Dr. Martin Hewson, Department of Political Science *Not present at defense

i

ABSTRACT

Anaerobic digestion involves biochemical and physiochemical processes. It is an

effective process for sludge stabilization and methane gas production. However, the

digester capacity decreases with time due to the buildup of solid deposits at the bottom of

the digester. Therefore, routine shutdown and cleanup are commonly practiced at

Wastewater Treatment Plants (WWTPs). Currently, WWTP operators face the challenge

of starting up an anaerobic digester in a timely, cost-effective manner with effective

methane gas production. This challenge can be addressed by computer modeling and

simulations of different operating conditions, which is a cost-effective solution in

comparison to experimentation. In this research, the commercially available BioWinTM

software was used to build an Anaerobic Digestion Model (BioWinTM

model) for the

simulation of the startup of an anaerobic digester. BioWinTM

dynamic simulations were

conducted under different operating conditions to determine optimal seed sludge volume,

sludge feed rate, and bicarbonate concentration to be used during the digester startup.

Strategies for a timely and cost-effective startup using the minimum amount of available

seed sludge and primary sludge feed were developed, based on the results of dynamic

simulations conducted using field data gathered from the Regina WWTP. The result of

this research was to decrease digester startup time and operational costs while increasing

methane gas production. This will provide significant economic and environmental

benefits, especially for WWTPs currently facing digester startup challenges, limited

sludge treatment capacities, and low methane gas production.

ii

ACKNOWLEDGEMENTS

I wish to express my appreciation toward my supervisor, Dr. Stephanie Young,

for her continuous support, encouragement, and guidance throughout the course of this

research and my studies at the University of Regina. Her vision and ambition contributed

to my motivation during this research project. Dr. Young made herself available to assist

me at all times and was always willing to share her knowledge and guidance with me. My

appreciation is also extended toward my co-supervisor, Dr. Christine Chan, who provided

me with valuable direction and the opportunity to explore my own inquires and to freely

develop this research.

I wish to express my deep appreciation and sincere thanks toward Alex Munoz

(Senior Process Engineer with Stantec Consulting Ltd.) for his continuous support,

valuable suggestions, endless and patient guidance, and assistance in overcoming the

technical problems encountered during this research. I would like to give special thanks

to Matthew Palmarin for his time, encouragement, and assistance.

I would also like to acknowledge the City of Regina Wastewater Treatment Plant

for providing the field data, and the Faculty of Engineering and Applied Science, and the

Faculty of Graduate Studies and Research for their financial support, which contributed

to the success of this research.

Finally, I wish to express a heartfelt thank you to my family for their everlasting

love and their unrelenting support for my graduate education.

iii

TABLE OF CONTENTS

ABSTRACT ......................................................................................................................... i

ACKNOWLEDGEMENTS ................................................................................................ ii

TABLE OF CONTENTS ................................................................................................... iii

LIST OF TABLES ............................................................................................................. vi

LIST OF FIGURES ......................................................................................................... viii

LIST OF ABBREVIATION .............................................................................................. xi

LIST OF APPENDICES .................................................................................................. xiii

1.0 INTROUDCTION ........................................................................................................ 1

1.1 Problem Statement .................................................................................................... 2

1.2 Objectives of the Research ........................................................................................ 6

1.3 Significance of the Research ..................................................................................... 7

2.0 LITERATURE REVIEW ............................................................................................. 8

2.1 Sludge Digestion ....................................................................................................... 8

2.1.1 Sludge ................................................................................................................ 9

2.1.2 Anaerobic Digestion ........................................................................................ 10

2.2 Anaerobic Digestion Startup ................................................................................... 17

2.3 Anaerobic Digestion Model Development ............................................................. 17

2.3.1 Anaerobic Digestion Model No.1 - IWA ......................................................... 18

2.3.2 BioWinTM

Model .............................................................................................. 20

2.4 Background of the Regina WWTP ......................................................................... 23

2.4.1 WWTP Processes ............................................................................................. 24

3.0 METHODOLOGY ..................................................................................................... 27

iv

3.1 Configuration of BioWinTM

Model ......................................................................... 28

3.2. Parameters for Calibration and Validation ............................................................ 29

3.2.1 Wastewater Characteristics – Data from the Regina WWTP .......................... 30

3.2.2 Wastewater Fractions - BioWinTM

................................................................... 32

3.2.3 Kinetic and Stoichiometric Parameters - BioWinTM

........................................ 34

3.3 Steady-state Calibration .......................................................................................... 34

3.4 Steady-state Validation ........................................................................................... 39

3.5 Dynamic Calibration ............................................................................................... 41

3.6 Dynamic Validation ................................................................................................ 45

4.0 RESULTS AND DISCUSSION ................................................................................. 48

4.1 Dynamic Simulation for the Startup of an Anaerobic Digester .............................. 48

4.1.1 Model Configuration ........................................................................................ 51

4.1.2 Dynamic Simulation ........................................................................................ 51

4.2 Optimization of Startup........................................................................................... 55

4.2.1 Optimization of Sludge Feed Rate ................................................................... 55

4.2.2 Optimization of Seed Sludge ........................................................................... 65

4.2.3 Optimization of Bicarbonate Concentration .................................................... 75

4.3 Development of Strategies for Optimal Digester Startup ....................................... 75

5.0 CONCLUSIONS......................................................................................................... 77

6.0 RECOMMENDATIONS ............................................................................................ 81

REFERENCES ................................................................................................................. 82

APPENDIX A ................................................................................................................... 88

APPENDIX B ................................................................................................................... 92

v

APPENDIX C ................................................................................................................... 96

APPENDIX D ................................................................................................................. 100

APPENDIX E ............................................................................................................... ..117

vi

LIST OF TABLES

Table 2.1 Summary and brief description of ADM1 ........................................................ 20

Table 2.2 Summary and brief description of anaerobic digestion in BioWin ................. 23

Table 3.1 Dimensions of each configuration.....................................................................29

Table 3.2 Measured and adjusted primary influent characteristics (the Regina WWTP,

2007)..................................................................................................................31

Table 3.3 Characteristics of Scum 1 and Scum 2 to the digesters .................................... 32

Table 3.4 Raw influent (sewage) wastewater fractions (EnviroSim Associates Ltd.) ...... 33

Table 3.5 Model kinetic parameters - Methanogens (EnviroSim Associates Ltd.) .......... 34

Table 3.6 Calibration - digesters effluent ......................................................................... 37

Table 3.7 Calibration - digesters operation and performance ........................................... 38

Table 3.8 Calibration - steady-state simulation results vs. plant measured results ........... 39

Table 3.9 Validation – digestes effluent ........................................................................... 39

Table 3.10 Validation - digesters operation and performance .......................................... 40

Table 3.11 Validation - steady-state simulation results vs. plant measured results .......... 41

Table 4.1 Situation of each model dynamic simulation for optimization of startup..........57

Table A-1: Raw influent variable for calibration - July, 2007...........................................88

Table A-2: Scums 1 & 2 variable for calibration - July, 2007 ......................................... .89

Table A-3: Sedimentation, splitter, gravity thickener and BFP variable for calibration -

July, 2007 ...................................................................................................... 91

Table B-1: Raw influent variable for validation - August, 2007.......................................92

Table B-2: Scums 1 & 2 variable for validation - August, 2007.......................................93

vii

Table B-3: Sedimentation, splitter, gravity thickener and BFP variable for validation -

August, 2007 .................................................................................................. .95

Table C-1: Raw influent constant..................................................................................... 96

Table C-2: Lagoon Dimension.......................................................................................... 96

Table C-3: Other units constant ........................................................................................ 96

Table C-4: Tertiary effluent .............................................................................................. 97

Table C- 5: Primary effluent to lagoons ........................................................................... 98

Table D- 1: Bicarbonate and seed constant for base case - April to September.............100

Table D-2: Raw influent variable for base case and Run #1 to Run #6 - April to

September, 2012 ......................................................................................... ..102

Table D-3: Sedimentation, splitter13, splitter14, gravity thickener, and BFP dewatering

variable for base case and Run #1 to #6 - April to September, 2012 ........... 108

Table D-4: Scum 2 variable for base case - April to September, 2012 .......................... 111

Table D-5: Bicarbonate variable for base case - April to September, 2012 ................... 112

Table D-6: Seed sludge 40 m3 for base case and Run #3 and #6 - April to September,

2012............................................................................................................... 114

Table E-1: F/M proportion sludge feed rate for Run #1, #2 and #3 - April to September,

2012............................................................................................................. 117

Table E-2: Flow proportion sludge feed rate for Run #4, #5 and #6 - April to September,

2012............................................................................................................... 119

Table E-3: Seed sludge for 80 m3 Run #2 and #5 - April to September, 2012 ............... 120

Table E-4: Seed sludge 120 m3 for Run #1 and #4 - April to September, 2012 ............. 122

Table E-5: Bicarbonate addition for Run #3 and #6 - April to September, 2012 ........... 124

viii

LIST OF FIGURES

Figure 2.1 Anaerobic digestion processes ...................................................................... ..13

Figure 2.2 Regina WWTP process flow diagram (courtesy of the City of Regina) ......... 25

Figure 3.1 Configuration of BioWinTM

model for calibration and validation...................28

Figure 3.2 Calibration - Digester 1 gas flow rate .............................................................. 43

Figure 3.3 Calibration - Digester 2 gas flow rate .............................................................. 43

Figure 3.4 Calibration - Digester 1 pH ............................................................................. 44

Figure 3.5 Calibration - Digester 2 pH ............................................................................. 44

Figure 3.6 Calibration - Digester 1 alkalinity ................................................................... 44

Figure 3.7 Calibration - Digester 2 alkalinity ................................................................... 44

Figure 3.8 Calibration - Digester 1 VFA .......................................................................... 45

Figure 3.9 Calibration - Digester 2 VFA .......................................................................... 45

Figure 3.10 Validation - Digester 1 gas flow rate ............................................................. 46

Figure 3.11 Validation - Digester 2 gas flow rate ............................................................. 46

Figure 3.12 Validation - Digester 1 alkalinity .................................................................. 46

Figure 3.13 Validation - Digester 2 alkalinity .................................................................. 47

Figure 3.14 Validation - Digester 1 VFA ......................................................................... 47

Figure 3.15 Validation - Digester 2 VFA ......................................................................... 47

Figure 3.16 Validation - Digester 1 pH ............................................................................ 47

Figure 3.17 Validation - Digester 2 pH ............................................................................ 48

Figure 4.1 Configuration of the BioWinTM

model for anaerobic digestion startup .......... 51

Figure 4. 2 Actual startup simulation - Digester 2 VFA ................................................... 52

Figure 4.3 Actual startup simulation - Digester 2 pH ....................................................... 52

ix

Figure 4.4 Actual startup simulation - Digester 2 gas flow rate ....................................... 52

Figure 4.5 Actual startup simulation - Digester 2 alkalinity ............................................. 53

Figure 4.6 Actual startup simulation - Digester 2 TSS......................................................53

Figure 4.7 Actual startup simulation - Digester 2 methane content ................................. 53

Figure 4.8 Sludge feed rate calculated on F/M proportion ............................................... 56

Figure 4.9 Sludge feed rate calculated on percent of digester volume proportion ........... 57

Figure 4.10 Run #1 simulation results of VFA ................................................................. 58

Figure 4.11 Run #1 simulation results of gas flow rate .................................................... 59

Figure 4.12 Run #1 simulation results of alkalinity .......................................................... 60

Figure 4.13 Run #1 simulation results of TSS .................................................................. 60













Figure 4.26 Run #3 simulation results of pH .................................................................... 70

x









Figure 4.35 Run #6 simulation results of pH .................................................................... 74

Figure C-1: Configuration of the Regina WWTP model...................................................96

xi

LIST OF ABBREVIATION

ADM Anaerobic Digestion Model

ADM1 IWA Anaerobic Digestion Model No. 1

ALK Alkalinity

ASM Activate sludge model

ASDM Activate sludge/anaerobic digestion model

BOD Biochemical oxygen demand

BOD5 Five-day biochemical oxygen demand

BFP Belt filter press

COD Chemical oxygen demand

DO Dissolved oxygen

HRT Hydraulic retention time

IFAS Integrated fixed film activated sludge

IWA International Water Association

LCFA Long chain fatty acid

MBBR Moving bed biofilm reactor

OUR Oxygen uptake rate

PS Primary sludge

PTP Primary treatment plant

SBR Sequencing batch reactor

SRT Solids retention time

TKN Total Kjeldahl Nitrogen

TS Total solids

TSS Total suspended solids

xii

VFA Volatile fatty acid

VSD Volatile solid destruction

VSS Volatile suspended solids

WAS Waste activated sludge

WWTP Wastewater treatment plant

xiii

LIST OF APPENDICES

Appendix A - Field Data from the Regina WWTP for Calibration

Appendix B - Field Data from the Regina WWTP for Validation

Appendix C - Steady-state simulation for the Regina WWTP

Appendix D - Field Data from the Regina WWTP and Model Data from

Calibration/Validation for Startup Simulation

Appendix E - Optimal Sludge Feed Rate, Seed and Bicarbonate Addition for Model Run

#1 to #6

1

1.0 INTROUDCTION

Anaerobic digestion is widely used for sludge stabilization for health and

aesthetic reasons in medium and large wastewater treatment plants (WWTPs). The

purposes of anaerobic digestion of sludge are to reduce pathogens, eliminate offensive

odors, and inhibit, reduce, or eliminate the potential for putrefaction. It is also used for

sludge volume reduction, production of usable methane gas for energy production, and

for improving sludge dewaterability.

The anaerobic digestion of sludge includes three biochemical and

physicochemical processes (Batstone et al., 2002): hydrolysis, acidogenesis

(fermentation), and methanogenesis. Therefore, strong skills are needed to startup and

operate an anaerobic digester. The skills can be gained from past experiences,

experiments, and computer simulations. In general, experimentation on anaerobic

digestion processes is expensive and time consuming. Alternatively, computer software

simulation is more cost-effective for optimizing the startup of the anaerobic digester

process and for predicting and then reducing operational problems, improving digestion

performance, and increasing biogas production (Dursun et al., 2011).

In this study, an Anaerobic Digestion Model was implemented into BioWinTM

software and is called the BioWinTM

model in this study. This is currently the most

widely used software in North America to optimize the startup and operation of anaerobic

digestion of sludge.

This chapter provides an introduction to the thesis. It includes a problem

statement on the startup and operation of anaerobic digesters in the Regina WWTP, and

the objectives and significance of the research.

2

1.1 Problem Statement

A large amount of concentrated sludge is produced in the Regina WWTP. The

sludge contains various organic and inorganic contaminants and pathogens. Two-stage

anaerobic digestion is currently used in the Regina WWTP to treat the primary sludge

removed from the upstream primary sedimentation process. Two high-rate anaerobic

digesters (No.1 and No.2) are coupled in a series with a third anaerobic digester (No. 3).

The first two tanks, operated in parallel, are used for sludge digestion and methane gas

production. They are heated and equipped with mixing facilities. The third digester is

unheated and used as a sludge thickener and for storage. It allows the sludge to be

separated from the supernatant and to be thickened before being pumped to the

dewatering facilities (City of Regina, 1994).

After several years of operation, the digester capacity decreases and digestion

performance becomes poor. Ideally, organic solids can be converted to digested sludge

and biogas at a constant rate in a complete-mixed digester. However, after several years

of operation, the digester shifts away from the complete mixing condition, because solid

contaminants deposit onto the wall and at the bottom of the digester, which reduces the

digester treatment capacity and further reduces mixing space and mixing efficiency.

There are several potential negative impacts of reduced mixing efficiency on the

anaerobic digestion process: (1) reduced sludge stability, (2) reduced biogas production,

(3) increased process instability due to overloading, (4) reduced pathogen kill, and (5)

inaccuracies in process data (Muller et al., 2011). Therefore, periodic shutdown and

cleaning of anaerobic digesters are needed.

3

After cleaning, an anaerobic digester should be put back into service as soon as

possible to reduce the chance of overloading the other digesters, and to produce methane

gas quickly to offset the natural gas cost ($1,000/day ) for power production during the

shutdown period. However, the operators of the Regina WWTP currently face a major

challenge of how to put back the digesters into service quickly and cost effectively. The

plant operators face the following challenges on startup: (1) it takes a long time (more

than 90 days) to start up the digester, and (2) the sludge in the digester becomes very

acidic (pH<4) during the startup period. The long startup time increases the chance that

the other digesters will become overloaded resulting in a bypass of untreated sludge to

the lagoon. Furthermore, operational costs are increased as natural gas is needed as a

substitute for methane gas during the shutdown period. A low pH of 4 might cause the

startup to fail, while also reducing methane gas production. As such further costs for

natural gas may be incurred.

The long startup time and low pH are attributed to the lack of seed sludge during

the initial startup period, which makes the startup process very complicated. A particular

problem is the initial sludge feed rate versus seed sludge concentration, which directly

determines the time required for a successful digester startup. The long startup time is

also attributed to the lack of proper guidance on criteria used for a cost-effective startup

of the anaerobic digester. To the author’s knowledge, no guiding principles are published

or made publically available. Most startup procedures for anaerobic digesters are based

on the skills and experience of the operators, and conservative rules of thumb. This

makes the startup time longer than necessary, thereby increasing the cost of the overall

sludge treatment process.

4

Therefore, there is a need to develop a computational tool to guide operators

through the startup process of anaerobic digesters quickly and economically, especially

when a lack of seed sludge is present at the initial startup period. Theoretically, the best

way to startup an anaerobic digester is to transfer seed sludge from another functioning

primary digester from the same plant. However, in the Regina WWTP, all digesters

encounter operating problems, and often no healthy sludge is available onsite for sludge

seeding. Therefore, seed sludge is often hauled to the Regina WWTP from the WWTP in

Saskatoon.

Hauling seed sludge from the WWTP in Saskatoon to the Regina WWTP is very

expensive. The capacity of the primary digester in Regina is 3,800 m3. Therefore, it is too

large to introduce a full amount of seed sludge for the startup of an anaerobic digester.

According to the information provided by the Regina WWTP, hauling 40 m3 of seed

sludge (about 1% of the digester’s capacity) from Saskatoon to Regina costs about

$4,000. A volume of 2,000 m3

of seed sludge is ideal to startup a digester quickly and

successfully. This amount would cost $200,000 to haul from Saskatoon to Regina and is

therefore not economically feasible. Therefore, the Regina WWTP elects to startup the

anaerobic digester by introducing a small amount of seed sludge (40 m3) rather than

2,000 m3 of seed sludge, which saves about $196,000. However, the startup process

becomes very long (more than 90 days) and operators encounter many operating

problems. One of the operating problems is a low pH, which makes the anaerobic

digester acidic. If the primary sludge feed rate is beyond the seed sludge acceptability, the

hydrolysis and fermentation processes produce too much acids which lower the pH of the

sludge in the anaerobic digester. The decrease of the pH is attributed to a low

5

concentration of methanogens, and the low pH conditions further inhibit methanogen

growth. Therefore, non-methanogens or acidogens (acid formers) became predominate,

which favors hydrolysis and acidogenesis (fermentation) reactions. This leads to the

excessive production of fatty acids, amino acids, and acetate. The low pH has a negative

impact on the anaerobic digestion process because it inhibits methanogen growth and

reduces the amount of methane gas production. When the pH is below 6.2, methanogenic

bacteria will no longer function (Filbert, 2012). This may cause the startup of the

anaerobic digester to fail. Therefore, pH is the most important factor affecting startup.

Adding bicarbonate increases pH and may be used to improve sludge digestion

performance.

The second problem is the uncertainty about the correlation between the primary

sludge feed rate versus seed sludge concentration during the startup period. The operator

feeds the raw sludge based on rules of thumb and past experience. Primary sludge feed

rate is another crucial factor in determining the success of the startup, because it directly

determines the startup time.

The third problem is the uncertainty about the optimal balance among the primary

sludge feed rate, the amount of seed sludge, and the bicarbonate concentration in order to

achieve a short and successful startup time of the anaerobic digester.

The fourth problem is that the Regina WWTP faces a challenge in effective

biogas production. One of the main purposes of anaerobic sludge digestion is to produce

methane to offset the energy costs. Methane can be used as a power source for: (1)

heating, mixing, and drying sludge, (2) heating the building, (3) incineration, and (4)

engine fuel. In the Regina WWTP, methane can be used as a digester heating source

6

which saves $1,000 per day in natural gas costs. However, the digesters in the Regina

WWTP were unable to treat 200 m3 of raw sludge per day before the digesters were

cleaned in 2012/2013, because the hydrolysis and fermentation steps produced excessive

acids which lowered the pH from 6.8 to 4. The acidic pH reduced the population of

methane formers and reduced methane production. Moreover, the acidic pH caused

corrosion on the concrete wall of the digesters, leading to tank leakage.

The fifth problem is the lack of a customized computer model of the Regina

WWTP to guide the operators during startup, to predict anaerobic digestion performance,

and to optimize the anaerobic digestion process.

In conclusion, the startup of an anaerobic digester is a very complicated process

because of the specific biochemical and physicochemical reactions involved (Batstone et

al. 2002). WWTPs face challenges when starting up and operating an anaerobic digester

cost-effectively. Therefore, there is a need to reduce the startup time when a minimum

amount of seed sludge is available. Therefore, the anaerobic digestion model built into

the BioWin software simulator was chosen for the development of anaerobic digester

startup strategies, the prediction of biogas production capacity, and the optimization of

anaerobic digester operation.

1.2 Objectives of the Research

The objectives of this study are to implement an Anaerobic Digestion Model into

the commercially available simulator software BioWinTM

, developed by EnviroSim

Associates Ltd. of Hamilton, Ontario, Canada. The BioWinTM

model will be used to

develop strategies that address the challenges mentioned above. As such, it will focus on

(1) decreasing startup time and increasing methane gas production by optimizing the

7

digester startup using optimal seed sludge amounts, and (2) optimizing primary sludge

feed rate and bicarbonate concentration so that the anaerobic digesters at the Regina

WWTP can be started up timely and cost-effectively. The BioWinTM

model will also be

used to optimize anaerobic digestion performance and to maximize its economic benefits

by increasing methane production, controlling acid formation and stabilizing the pH of

the sludge, etc.

This study is divided into four stages: (1) collection and compilation of field data

from the Regina WWTP, (2) model calibration, validation, and simulation, (3)

determination of the optimal operating parameters for the Regina WWTP to startup and

operate the anaerobic digesters cost-effectively, and (4) development of general startup

strategies that can be applied to other WWTPs. The parameters to be optimized include:

amount of microorganism seed sludge, primary sludge feed rate, the ratio of primary

sludge feed rate versus microorganism seed sludge concentration, pH, alkalinity, etc. The

BioWinTM

model used in this study can be further implemented to anaerobic digestion

processes in other WWTPs.

1.3 Significance of the Research

The significance of this thesis research is to develop strategies and provide criteria

for the cost-effective startup and operation of an anaerobic digester by decreasing the

startup time and therefore improving early methane gas production. This will reduce the

operating costs of anaerobic sludge digestion and plant energy costs. In addition, it will

increase the overall efficiency of anaerobic digestion system in terms of maximizing the

decomposition of contaminants, minimizing pathogen contamination, and improving odor

control and sludge dewaterability. Moreover, it will provide significant economic and

8

environmental benefits around the world, especially for old WWTPs currently facing

sludge digestion problems, limited sludge treatment capacities, and low methane gas

production, etc.

2.0 LITERATURE REVIEW

2.1 Sludge Digestion

Sludge digestion is a biological process in which organic solids are decomposed

into stable substances through the activities of either anaerobic or aerobic organisms.

Digestion reduces the total mass of solids, destroys pathogens, and makes it easier to

dewater and dry the sludge. Sludge digestion can be applied in two ways: anaerobic

digestion and aerobic digestion.

Aerobic digestion is the process of oxidizing and decomposing the organic part of

the sludge by microorganisms in the presence of oxygen. The end products of aerobic

digestion are primarily carbon dioxide and water which are the stable oxidized forms of

carbon and hydrogen. If the biodegradable starting material contains nitrogen,

phosphorus and sulfur, then the end products may also include their oxidized forms -

nitrate, phosphate and sulfate. In aerobic wastewater treatment systems, microorganisms

feed on organic materials and reduce the suspended solids in the wastewater. Therefore,

aerobic digestion is capable of reducing mass and volume, and reducing pathogenic

organisms. It also has some key advantages for smaller plants when compared to

anaerobic digestion, such as low capital cost and simple operational control.

Anaerobic digestion is based on the biological conversion of degradable

compounds in the absence of oxygen. The end products of this process may include

methane and carbon dioxide. Anaerobic digestion has been widely used for sludge

9

digestion and it is a promising method for solving some energy and ecological problems.

This digestion process is the one that represents the subject of this paper. The process is

used in wastewater treatment plants with the purpose of organic substance and pathogen

reduction. The advantage of using anaerobic digestion as a stabilization method for

sludge is that it generates biogas, biogas that can be used in a cogeneration unit to obtain

the thermal energy needed for the anaerobic digestion in addition to supplying a part of

the electrical energy consumed in the wastewater treatment processes. The only drawback

is hydrogen sulfide, a gas that in contact with oxygen develops sulfuric acid, a strong

mineral acid, which can damage the engines (Manea, 2012). Another strategy used in

developed countries is to obtain products from wastewater treatment sludge that can be

sold, for example recovering valuable components of sludge, such as metals and chemical

compounds.

2.1.1 Sludge

During the treatment processes, solids are removed from wastewater. The sludge

from wastewater treatment plants is derived from primary, secondary and/or tertiary

treatment processes. Most often, the produced sludge has a small concentration and a

high biodegradable load. Digested sludge is inoffensive, having the appearance and

characteristics of a rich potting soil.

Primary sludge is produced following primary treatment, which usually entails the

sedimentation. Suspended solids which are removed from liquids by gravitation settling

in sedimentation tanks form sludge. This primary sludge needs to pass through additional

treatments such as anaerobic digestion in order to reduce its water content, stabilize its

organic matter, reduce its odors, reduce its pathogen load, and reduce its volume and

10

global mass (Manea et al., 2012). Primary sludge treatment in anaerobic digesters is the

topic of this thesis. Secondary sludge is generated from the biological treatment stage.

This stage usually has the purpose of decomposing organic matter through biological

processes. The most often used is the activated sludge process, where the wastewater is

aerated in an aeration basin in which microorganisms metabolize the suspended and

soluble organic matter. At the end of the process, the treated water has to go through a

clarifier in order to be separated from the activated sludge. A part of the sludge separated

in this secondary clarifier returns to the biological treatment stage. The rest of the sludge,

not necessary for the optimum development of the biological processes, is surplus

activated sludge. In the sludge treatment stage of the wastewater treatment plant, the

primary and secondary sludge described above are usually mixed together generating a

type of sludge referred to as a mixed sludge. This sludge has to pass through additional

treatments in order to reduce its water content, stabilize its organic matter, reduce its

odors, reduce its pathogen load, and reduce its volume and global mass.

2.1.2 Anaerobic Digestion

A Frenchman, Mouras, applied anaerobic digestion for the first time to treat

wastewater in his invention of a crude version of a septic tank in 1881, named by him as

the “automatic scavenger” (McCarty et al., 1982 ). Subsequently an Englishman,

Cameron, constructed a tank in 1895 which was similar to Mouras’s “automatic

scavenger” but with better treatment efficiency, and termed it the “septic tank.” Because

of the successful results achieved in using these tanks, the local government of Exeter in

1897 approved the treatment of the entire city’s wastewater by these septic tanks.

Moreover, the value of the methane gas which was generated during sludge

11

decomposition in the septic tanks was recognized by Cameron, and some of the gas was

used for heating and lighting purposes at the disposal worksites (Chawla, 1986 ). During

most of the following century, the development of anaerobic digestion technology

remained exclusively linked to the stabilization of putrescible solids from domestic

wastewaters. This led to the design of heated, fully mixed, reactors of the type widely

used today for the digestion of sewage sludge and animal manures. Application of

anaerobic digestion systems to industrial wastewater depollution was stimulated by the

rise in fossil fuel prices in the early 1970s and by the increasingly stringent pollution

control regulations. The unsuitability of the conventional mixed digester for the treatment

of industrial wastewaters of low-strength and of largely soluble organic material, led to

the concept of biological solids recycling and to the retention of active biomass within the

digester (Abbasi et al., 2012). Anaerobic digestion is now considered a consolidated

technology with more than 2200 high-rate reactors implemented worldwide (Van Lier,

2008). In Europe, between 1995 and 2010, the number of plants installed increased from

15 to 200, which implies an installation capacity rise to nearly 6,000,000 tons per year

(from 200,000 tons per year) (de Baere et al., 2010). Moreover, the number of anaerobic

digester is expected to increase due to both climate change awareness and the significant

boost in the use of renewable energy.

2.1.2.1 Anaerobic Digestion Processes

Anaerobic digestion is a significant process in wastewater treatment processes. It

includes a sequence of biochemical processes under anaerobic conditions for organic

matter degradation by various microorganisms. The byproduct of anaerobic digestion -

methane (CH4) - is a rich source of clean energy, which contributes to environmental

12

conservation and sustainability as oppose to fossil fuels (Chen, 2010). Therefore,

anaerobic digestion is widely used as an attractive means for wastewater treatment

around the world while more and more new process configurations are continuously

being developed.

Anaerobic systems are quite complicated, entailing many chains of interconnected

biological reactions. There are multistep reactions involved in anaerobic digestion for

degrading complex biodegradable materials. Generally three types of chemical and

biochemical reactions are involved in anaerobic digestion: hydrolysis, fermentation, also

called acidogenesis (the formation of soluble organic compounds and short-chain organic

acids), and methanogenesis (the conversion of organic acids and hydrogen into methane

and carbon dioxide) (Tchobanoglous et al., 2003).

The coordinated steps or chemical reactions start with hydrolysis, in which

complex organic materials are hydrolyzed and reduced to small soluble organic

substrates. Fermentation reactions are then carried out to convert amino acids, simple

sugars, and long chain fatty acids into short-chain fatty acids, including acetic acid. Co-

generative products included formic acid, acetic acid, propionic acid and ethanol (Oh et

al., 2003).

Acetic acid, propionic acid, and H2 are end products of the anaerobic oxidation of

long chain fatty acids (Shin et al. 2003). Another pathway for acetic acid generation is

acetogenesis from short-chain fatty acids (McCarty &Mosey, 1991). Short-chain

fatty acids such as formic acid, propionic acid, lactic acid, butyric acid, and pyruvic

acid are resources for acetic acid formation. While carbon dioxide and hydrogen are

generated as initial biogas components, methane becomes the dominant gas later due

13

to methanogensis. This process includes acetotrophic methanogensis, which converts

acetate to methane and hydrogenotrophic methanogensis, which converts carbon dioxide

and hydrogen gas to methane. Details of the anaerobic digestion processes (Chen, 2010)

are presented in follow:

Complex Biodegradable Particulates

Figure 2.1 Anaerobic digestion processes

2.1.2.2 Anaerobic Digestion Microbiology

Anaerobic digestion is a complex process that is performed by a variety of

microorganisms. Both Archaea and Bacteria are involved in anaerobic digestion. The

Hydrolysis

Fermentation

Volatile acids Acetic acids

(short chain)

Hydrogen Hydrogen

Methanogenesis

Methane & Carbon dioxide

Proteins Amino acids

Carbohydrates Simple carbohydrates (sugars)

Fats Long chain fatty acids

14

three major groups of anaerobic microorganisms in a digester population include: (1)

hydrolytic fermentative bacteria, (2) syntrophic acetogenic bacteria, and (3) Archaea

(methanogens). The consortia of microorganism involved in the overall conversion of

complex organic matter to methane begins with bacteria that hydrolyze complex organic

matter – such as carbohydrates, proteins, and fats – into simple carbohydrates, amino

acids, and fatty acids. The simple carbohydrates and acids are then utilized to obtain

energy for growth by fermenting bacteria, producing organic acids and hydrogen as the

dominant intermediate products. The organic acids are then partially oxidized by other

fermenting bacteria which produce additional hydrogen and acetic acid. Hydrogen and

acetic acid are the main substrates used by Archaea methanogens, which convert them

into methane (Rittmann & McCarty, 2001). The chemical equations are shown as

follows:

4H2 + CO2 → CH4 + 2H2O

4HCOO- + 4H

+ → CH4 + 3CO2 + 2H2O

4CO + 2H2O → CH4 + 3CO2

4CH3OH → 3CH4 + CO2 + 2H2O

4(CH3)3N + H2O → 9CH4 + 3CO2 + 6H2O + 4NH3

CH3COOH → CH4 + CO2

2.1.2.3 Effects Parameters on Anaerobic Digestion

The successful startup and operation of an anaerobic system requires a proper

balance between the hydrolytic and fermentative organisms involved in the first step and

the methanogenic organisms responsible for the second step. This balance is

accomplished through proper seeding, as well as through the control of organic-acid

15

production and pH during startup, when the microbial populations are establishing

themselves. Therefore, environmental factors affect the operational performance of

anaerobic digestion. General characteristics of anaerobic digestion include mixed

suspended solids, complex microorganism communities, long hydraulic and solids

residence time (30-60 days), and mesophilic temperature (35° C) (Tchobanoglous et al.,

2003). Important environmental factors in anaerobic digestion processes include

(Tchobanoglous et al., 2003): (1) solids retention time, (2) hydraulic retention time, (3)

temperature, (4) alkalinity, (5) pH, (6) the presence of inhibitory substances, i.e., toxic

materials, and (7) the bioavailability of nutrients and trace metals. The first three factors

are important in process selection. Alkalinity is a function of feed solids and is important

in controlling the digestion process. The rest of the parameters are discussed below.

Solids Retention Time

Solids retention time (SRT) is the average time the solids are held in the digestion

process, and the hydraulic retention time is the average time the liquid is held in the

digestion process. For soluble substrates, the SRT can be determined by dividing the

mass of solids in the reactor (M) by the mass of solids removed daily (M/d). The

hydraulic retention time is equal to the volume of liquid in the reactor (L3) divided by

the quantity of biosolids removed (L3/d). For a digestion system without recycling, SRT

= . The three reactions (hydrolysis, fermentation, and methanogenesis) are directly

related to SRT (or ). There is a minimum SRT for each reaction. If the SRT is less than

the minimum SRT, bacteria cannot grow rapidly enough and the digestion process will

fail eventually.

Temperature

16

In anaerobic digestion, temperature is important in determining the rate of

digestion, particularly the rates of hydrolysis and methane formation. Most anaerobic

digestion systems are designed to operate in the mesophilic temperature range, between

30 and 38° C (85 and 100°F). While the selection of the design operating temperature is

important, maintaining a stable operating temperature is more important because the

bacteria, especially the methane formers, are sensitive to temperature changes. Generally,

temperature changes greater than 1° C /d affects process performance, and changes of

less than 0.5° C /d are recommended.

Alkalinity

A well-established digester has a total alkalinity of 2000 to 5000 mg/L. The

principal consumer of alkalinity in a digester is carbon dioxide, and not volatile fatty

acids as commonly believed. Carbon dioxide is produced in the fermentation and

methanogenesis phases of the digestion process. Due to the partial pressure of the gas in

the digester, the carbon dioxide solubilizes and forms carbonic acid, which consumes

alkalinity. The carbon dioxide concentration in the digester gas is therefore reflective of

the alkalinity requirements. Supplemental alkalinity can be supplied by the addition of

sodium bicarbonate, lime, or sodium carbonate.

pH

pH is an important factor in maintaining functional anaerobic digestion. A typical

pH is in the range of 6.5-7.6 (Parkin and Owen 1986). The accumulation of intermediate

acids leads to a pH drop during fermentation. In order to maintain a stable operation, it is

necessary to add bicarbonate or carbonate as an alkalinity buffer to neutralize volatile

fatty acids and carbon dioxide (Chen, 2010).

17

2.2 Anaerobic Digestion Startup

During reactor startup, the operator must maintain a sufficiently small loading on

the reactor so that organic acids produced by the much faster growing fermentative

bacteria do not exceed the buffering capacity of the system. If this occurs, the pH will

drop and the methanogenic population may die. According to Rittmann and McCarty

(Rittmann & McCarty, 2001) the crucial steps during startup are: (1) begin with as much

good anaerobic seed as possible, (2) fill the digester with this seed and water, (3) bring

the system up to temperature, (4) add buffering material in the form of a chemical, such

as sodium bicarbonate, to protect against a pH drop, and (5) add a small amount of

organic waste sufficient to let the organic acid content from fermentation reach no more

than 2000 to 4000 mg/l while keeping the pH between 6.8 and 7.6. These organic acids

are a food source required for the methanogenic population to grow.

2.3 Anaerobic Digestion Model Development

Anaerobic digestion modeling is a recognized and widely used tool across all

anaerobic digestion technology and scientific activities. Mathematical modeling of the

anaerobic digestion process was motivated by the need for efficient operation of

anaerobic systems in the early 70’s (Hill & Barth, 1997). The first models were relatively

simple due to the limited knowledge about the process. Experimental investigation,

system analysis, and increased computing power led to the development of much more

detailed models in recent years. It is not the goal of this review to list all available models

for anaerobic digestion, but a brief overview is given in the following paragraphs.

The first modeling approaches focused on describing the rate limiting step of the

process, considering that anaerobic digestion is a multistep process where one slower step

18

controls the global rate (Hill & Barth, 1997). The limiting step can, however, be different

under different operating conditions. Some authors considered methanogenesis as the

limiting step or the conversion of fatty acids into biogas or the hydrolysis of suspended

solids. These series of models were simple and easy to use but were unable to adequately

describe the process performance, especially under transient conditions. A second

generation of models considered the concentration of volatile fatty acids as the key

parameter, incorporating acidogenesis and acetogenesis separately (Hill, 1982). The

hydrogen partial pressure, as a key regulatory parameter influencing the redox potential

in the liquid phase, and bacterial groups, with differentiated acetoclastic and

hydrogenotroph methanogens, were included in several models. The redox potential is a

function of the hydrogen partial pressure and determines the volatile fatty acid (VFA)

production in this family of models.

Recently, computer modeling is able to support the increased application of

anaerobic technology as a sustainable waste treatment option and a viable alternative to

other energy generating processes. These models incorporate additional processes and

species, more detailed kinetics with inhibition, and consideration to different substrates

(Donoso-Bravo et al., 2011).

2.3.1 Anaerobic Digestion Model No.1 - IWA

A recent development in anaerobic digestion modeling is the landmark model

named Anaerobic Digestion Model, No.1 (ADM1) that was developed by the

International Water Association (IWA) Task Group (Boltes et al., 2008). It was

established in 1997 with the goal of developing a generalized anaerobic digestion model

and to reach a common basis for further model development and validation studies with

19

comparable results. This model supplied a fundamental basis for kinetic modeling of

anaerobic digestion with a set of default kinetic parameters. The structured model

includes multiple steps describing biochemical as well as physicochemical processes. The

biochemical steps include disintegration from homogeneous particulates to

carbohydrates, proteins and lipids; extracellular hydrolysis of these particulate substrates

to sugars, amino acids, and long chain fatty acids (LCFA), respectively; acidogenesis

from sugars and amino acids to VFAs and hydrogen; acetogenesis of LCFA and VFAs to

acetate; and separate methanogenesis steps from acetate and hydrogen/CO2 (Batstone et

al., 2002).

Several benefits were expected from the development of this first generic model

of anaerobic digestion (Batstone et al., 2002):

• Increased model application for full-scale plant design, operation, and

optimization

• Further development work on process optimization and control, aimed at direct

implementation in full-scale plants

• Common basis for further model development and validation studies to make

outcomes more comparable and compatible

• Assisting technology transfer from research to industry

ADM1 has become available in Matlab and Simulink, but also in specific water

related simulation software, such as WEST, BioWinTM

and Aquasim (Lauwers et al.,

2013).

A summary and brief description of the studies found in the literature about

modeling in anaerobic digestion systems are shown below:

20

Table 2. 1 Summary and brief description of ADM1

Reference Model Measurements

Batstone, D.J., et al. 2009 ADM1 - IWA Biogas, VSS

Palatsi, J., et al. 2010 ADM1 - IWA Methane, acetic, butyric, Propionic

acid

Flotats, X., et al. 2003 ADM1 - IWA Acetal, propionate, valerate,

methane

Batstone, D.J., et al. 2003 ADM1 - IWA VFA, biogas, pH, methane content

Kalfas, H., et al. 2006 ADM1 - IWA TSS, VSS, COD, VFA, biogas,

gas composition, pH

Koch, K., et al. 2010 ADM1 - IWA Biogas, gas composition, NH4,

NKT, VFA, alkalinity, TS

2.3.2 BioWinTM

Model

BioWinTM

is a wastewater treatment process simulator. It is used for designing,

upgrading, and optimizing wastewater treatment plants. The package was developed with

the primary objective of providing a powerful tool to aid both the process designer and

the operators of these facilities.

According to the BioWinTM

software manual (EnviroSim Associates Ltd.), most types

of wastewater treatment systems can be configured in BioWinTM

using many process

modules:

A range of activated sludge bioreactor modules – suspended growth reactors

(diffused air or surface aeration), various SBRs, media reactors for IFAS and

MBBR systems, variable volume reactors

Anaerobic and aerobic digesters

Various settling tank modules – primary, ideal, and 1-D model settlers

21

Different input elements – wastewater influent (COD- or BOD-based), user-

defined (state variable concentrations), metal addition for chemical

phosphorus precipitation (ferric or alum), methanol for denitrification

Other process modules – holding tanks, equalization tanks, dewatering units,

flow splitters, and combiners

BioWinTM

is a Chemical Oxygen Demand (COD) based simulation. COD is a

measure of wastewater strength, specifically the electron donating capacity of organic

material. In the BioWinTM

software, characterization of the carbonaceous material in

municipal wastewater is in terms of the COD. This selection is based on a number of

factors, but primarily because COD provides a consistent basis for describing of the

activated sludge process, and for quantifying sludge production, oxygen demand, etc.

However, Biochemical Oxygen Demand (BOD) is the test that measures the portion of

organic substrate utilized for energy generation and ignores the portion transformed into

new cell mass. Therefore, the suitability of COD is established by considering the

utilization of organic substrate and can be used as the basis for a mass balance.

The BioWinTM

software suite presently includes two modules (EnviroSim

Associates Ltd.):

1. A steady state module for analyzing systems based on constant influent

loading and/or flow weighted averages of time-varying inputs. This unit is

also very useful for mass balancing over complex plant configurations.

2. An interactive dynamic simulator where the user can operate and

manipulate the treatment system "on the fly". This module is ideal for

22

training and for analyzing system response when subjected to time-

varying inputs or changes in operating strategy.

This complete model approach frees the user from having to map one model’s

output to another model’s input which significantly reduces the complexity of building

full plant models, particularly those incorporating many different process units.

The anaerobic digestion model in BioWin contains the following functional categories

(EnviroSim Associates Ltd.):

(1) Heterotrophic growth through fermentation which achieves VFA generation.

There are two pathways for the fermentation of readily biodegradable substrate to

acetate, propionate, carbon dioxide, and hydrogen. The dominant pathway is governed by

the dissolved hydrogen concentration. These processes are mediated by the ordinary

heterotrophic organisms. This base rate is modified to account for nutrient limitations

(ammonia, phosphate, other cations and anions) and pH inhibition.

(2) Growth and decay of propionic acetogens in order to conduct anaerobic digestion.

These two processes describe the growth and decay of propionic acetogens,

converting propionate to acetate, CO2, and hydrogen. This base rate is modified to

account for environmental conditions (anaerobic conditions, inhibition by hydrogen and

acetate), nutrient limitations (nitrogen, phosphate, other cations and anions), and pH

inhibition. The decay process has a rate that varies according to the electron acceptor

environment.

(3) Growth and decay of methanogens in anaerobic digestion.

These processes describe the growth and decay of two of the principal groups of

obligate anaerobic microorganisms (acetoclastic methanogens), converting acetate or

23

methanol to methane and CO2; and hydrogenotrophic methanogens, converting CO2 or

methanol and hydrogen to methane and water.

This base rate is modified to account for nutrient limitations (ammonia, phosphate,

other cations and anions) and pH inhibition. For both populations, the decay rate varies

according to the electron acceptor environment.

Table 2. 2 Summary and brief description of anaerobic digestion in BioWinTM

Reference Model Substrate Measurements

Dursun, D., et al.,

2011

ASDM - BioWinTM

PS + WAS Biogas

Parker, W.J., et al.

2008

ASM & ADM -

BioWinTM

WAS COD, OUR, TSS, VSS,

methane

Parker, W.J., et al.

2007

ADM - BioWinTM

WAS Biogas

Jones, R., et al. 2008 ASM - BioWinTM

WAS COD, TSS, VSS, OUR,

methane, TKN, NH3

Muller, C., et al. 2011 Integrated Model -

BioWinTM

PS + WAS VSD

Liwarska-Bizukojc,

E., et al. 2010

ASM - BioWinTM

WAS Modified parameters

note: ASDM: activated sludge/anaerobic digestion model; ASM: activated sludge model.

2.4 Background of the Regina WWTP

The Regina WWTP is located west of the City of Regina. On average, it treats 70

to 75 ML/d of wastewater prior to discharging it to the Wascana Creek. The plant is

capable of reducing the environmental impact of four main pollutant types: (1) organic

and inorganic suspended solids measured as total suspended solids (TSS), (2) soluble

24

organic material or Biochemical Oxygen Demand (BOD), (3) pathogenic microorganisms

and bacteria for which fecal coliform is used as an indicator organism, and (4) the macro-

nutrient phosphorus which has been implicated in causing downstream eutrophication.

The plant achieves excellent effluent quality:

• BOD < 16 mg/L

• TSS < 20 mg/L

• Total Phosphorus < 1.0 mg/L

• Fecal Coliforms <100 Coliforms /100 ML (April through October)

However, only about 15 to 20% of the total nitrogen is removed from the

wastewater. This is done through biological treatment, where nitrogen-bearing

compounds are incorporated into bacteria, protozoans, and algae cells in lagoons. A large

portion of the nitrogen in the wastewater is discharged into Wascana Creek in the form of

ammonia. The City of Regina is currently in the process of selecting a consortium for the

upgrade of the plant to reduce ammonia and nutrients to lower limits.

2.4.1 WWTP Processes

Briefly stated, wastewater from the City of Regina is conveyed through four

distinct processes, as presented in Figure 2.1:

1) Preliminary treatment by screening and grit removal.

2) Primary treatment by settling 50 to 60% of the organic solids.

3) Secondary treatment by biodegradation in aerated lagoons for 90%

removal of organic matters and solids.

25

4) Tertiary treatment by alum addition and chemical precipitation of

phosphorus, algae and pathogens; seasonal effluent disinfection by UV

irradiation to meet bacteriological discharge standards.

Figure 2. 2 Regina WWTP process flow diagram (courtesy of the City of Regina)

Preliminary Treatment Process

Raw sewage is collected at the McCarthy Blvd. Pump Station , where it is

screened before being pumped through two 1,050 mm steel force mains to the Primary

Treatment Plant (PTP) located 5km west of the McCarthy Blvd. Pump Station. At the

PTP screened sewage undergoes grit removal where suspended solids are removed using

two aerated grit removal tanks and three primary sedimentation tanks. Within the grit

removal tanks, heavy inorganic solids are settled and the liquid is aerated and mixed by

26

compressed air. Every one to two months the flow is diverted and the grit is removed,

flushed, loaded onto trucks and hauled to the landfill.

After the grit removal operation, wastewater flows by gravity over a weir into the

primary sedimentation tanks. At this stage the flow velocity is slowed down through three

large rectangular tanks, thereby permitting separation of solids by settling and scum by

flotation. Primary effluent is then pumped to the aerated lagoons located 2km from the

preliminary treatment process. Sludge and scum are collected every two hours by a

bridge collector mechanism that travels at a low speed. The scum and sludge are then

pumped to the digesters, undergoing biological stabilization under mesophilic anaerobic

conditions in the primary digesters. The digestion results in the production of methane

and other gases. The methane gas is utilized in two ways. One way is to compress the gas

and inject it back into the digesters for mixing. The other way is to use it to heat the

buildings by burning it in a dual feed boiler. This also helps off-set potential costs for

burning natural gas. After 18 days, the digested sludge is then pumped to the dewatering

process where the digested sludge is centrifuged to reduce its water content before

disposal. The dewatering processes produce two streams: a solids stream called “cake”

and a liquid stream known as “concentrate”. Concentrate is pumped to the influent

channel of the sedimentation tanks. The cake is conveyed via a belt conveyor onto a truck

and hauled to an onsite stockpile located one kilometer away from the plant.

Secondary Treatment Process

Secondary treatment within the Regina WWTP is comprised of five aerated

treatment lagoons (Cells 2A, 2, 3, 1S, and 4). The aerated lagoons consist of earth basin

bioreactors with submerged aeration supporting a suspended growth activated sludge

27

process with a mixed liquor concentration below 800 mg/L. In this process, mainly

organic carbon is degraded by aerobic bacteria and higher organisms in the sludge.

It is common practice in lagoon systems to divide the wastewater into two streams at the

valve chamber. Between 10 to 20% of the primary effluent is sent to cell 1S. The rest of

the primary effluent is sent to cells 2A, 2 and 3. The effluent from cells 1S and 3 are

discharged to cell 4 for storage before it is conveyed by gravity to the tertiary treatment

plant for further treatment.

Tertiary Treatment Process

After biological treatment in the lagoons, the lagoon effluent flows by gravity into

the tertiary clarifier where it is treated with alum and polymer to precipitate phosphorus,

suspended solids, algae, and pathogens. In the clarifier, the flow velocity is slowed down,

permitting separation of solids by settling and scum by flotation. Tertiary effluent is then

conveyed by gravity to the UV disinfection system before being discharged to Wascana

Creek. Sludge is collected continuously by a sludge scrapper and pumped to a sludge

storage lagoon. Scum is periodically withdrawn by a sewer truck and discharged into the

cell.

3.0 METHODOLOGY

In this study, BioWinTM

model was used to simulate the startup of an anaerobic

digester. BioWinTM

is a widely recognized commercially available dynamic modelling

and simulation package for wastewater treatment processes used in North America. It

was developed by the EnviroSim Associates of Canada.

This chapter presents an analysis of the startup process for an anaerobic digester.

It includes a discussion of the five stages of the simulation process: (1) configuration of

28

the wastewater treatment process in the BioWinTM

; (2) data collection for the calibration

of the BioWinTM

model; (3) calibration of the BioWinTM

model; (4) validation of the

BioWinTM

model; and (5) application of the BioWinTM

model to achieve the objectives of

the research. The objectives of the research are to develop strategies for the timely and

cost-effective startup and operation of anaerobic digesters, and to maximize methane gas

production.

3.1 Configuration of BioWinTM

Model

The configured of BioWinTM

model is presented in Figure 3.1. The dimensions of

each component are summarized in Table 3.1. As shown in Figure 3.1, the configuration

of the primary treatment processes consists of the following components: a raw influent

element, a grit tank element, grit icons, a sedimentation tank element, primary effluent to

lagoons icon, scum 1 and scum 2 elements, digester 1 and digester 2 elements, gravity

thickener elements, dewatering elements, and a cake icon. Figure 3.1 also shows that

mixer 1 is located between the grit tank and the sedimentation tank. Mixer 2 is located

between the scum 1 element and the digester 1 element. Mixer 3 is located between the

scum 2 element and the digester 2 element. A flow splitter is located between the

sedimentation tank and the scum 1/scum 2 elements.

Figure 3.1 Configuration of BioWinTM

model for calibration and validation

29

Table 3.1 Dimensions of each configuration

Volume

m3

Area

m2

Depth

m

Width

m

Head space

volume m3

Head space

pressure KPa

Grit tank 940 235 4 4

Primary

sedimentation tank

7,582 2,106 3.6

Digester No.1 3,796 406 9.36 492 103

Digester No.2 3,390 406 8.36 492 103

Gravity thickener 3,796 406 9.36

3.2. Parameters for Calibration and Validation

The BioWinTM

model developed in this study was calibrated using the Regina

WWTP testing data collected in July 2007. In this case, detailed data concerning the

organic fraction of the influent wastewater, measured as COD, was not available.

Therefore, literature data, default values, and assumptions were used to set influent

wastewater characteristics.

In calibrating the model to deal with small unconformities, it was necessary to

make small adjustments on certain parameters in the model until the predicted results

matched the measured plant performance. The parameters which can be adjusted are the

reliable data which are not available from data collection and have large effects on the

model simulation. Therefore, engineering knowledge and experiences were used to

determine these important parameters. However, it is not enough to understand if a

certain parameter plays an important role in the model performance without a sensitivity

analysis. Literature sensitivity analysis results, engineering experiences and knowledge,

and model defaults were used in model calibration.

30

Anaerobic digestion parameters used in model calibration included wastewater

fractions, operational parameters, kinetic parameters, and stoichiometric parameters.

Wastewater fractions were used to specify the fractional composition of the influent

wastewater. Kinetics parameters in BioWinTM

stand for the rate of conversion reaction.

Stoichiometric parameters in BioWinTM

stand for the ratio of conversion of different

compounds (EnviroSim Associates Ltd., Canada). Default stoichiometric parameters in

BioWinTM

are tailored for municipal wastewater treatment systems (EnviroSim

Associates Ltd., Canada) which can be used directly for this research without any

adjustment.

3.2.1 Wastewater Characteristics – Data from the Regina WWTP

The Regina WWTP data collected between July and August of 2007 were

selected for model calibration and validation for two reasons: (1) the performance of the

digesters in 2007 were stable, which provided good historical data for calibration and

validation, (2) the characteristics of the wastewater in the summer (July and August)

showed good variation; therefore, it helped to evaluate the flexibility of the digesters in

the Regina WWTP.

Table 3.2 summarizes the measured and adjusted primary influent characteristics

between July and August of 2007. In assessing the validity of data, it is often useful to

examine the ratios of certain parameters rather than the absolute values of the parameters

themselves. The expected COD/BOD5 ratio for typical domestic wastewater should be in

the range of 2.0 to 2.2 and 1.9 to 2.1 for raw sewage and primary effluent, respectively

(Melcer et al., 2003).

31

Table 3.2 Measured and adjusted primary influent characteristics (the Regina WWTP,

2007)

Name Influent characteristics

July 2007

Influent characteristics

August 2007

Measured Adjusted Measured Adjusted

Total COD

mgCOD/L

243 455 241 457

TKN mgN/L 34.6 32.35

Total P mgP/L 5.53 5.25

Nitrate N mgN/L 7.79 0.01

pH 7.53 7.49

Alkalinity mmol/L 5 4.88

Inorganic S.S.

mgISS/L

55 43

Calcium mg/L 79 92

Magnesium mg/L 29 22

TSS mgTSS/L 232 218

VSS mgVSS/L 178 177

NH3-N 24.7 22.2

BOD5 mg/L 182 227.5 183 228.75

Flow m3/d 67,910

DO mg/L 0

Ratios

COD : BOD5 1.34 2 1.32 2

COD : VSS 1.37 2.56 1.36 2.58

VSS : TSS 0.73 0.81

TKN : COD 0.14 0.076 0.13 0.071

NH3-N : TKN 0.71 0.69

According to the Regina WWTP 2007 annual report, BOD5 was 182 mg/L, but

was adjusted to 227.5 mg/L because the Regina per capita BOD5 generation was

consistently lower than 0.08 kg/cap/d when no kitchen sink grinders were used. Thus

BOD5 = 0.1/0.08 ×182=227.5 (Tchobanoglous, et al., 2003). Moreover, the measured

BOD5 to COD ratio was 1.29, which was much lower than the normal ratio. In this case,

BOD5 was adjusted to 227.5 mg/L, and COD was adjusted to 455 mg/L by using a

32

COD/BOD5 ratio of 2.0. As such, the same method was used to adjust BOD5 and COD

measured in August of 2007 to 228.75 mg/L and 457 mg/L, respectively.

In the Regina WWTP, the scum collected in the primary sedimentation tanks is

also pumped to the digester, which contributes to biogas production. Therefore, scum 1

and scum 2 were considered two other streams of influent to the digesters, besides the

primary influent.

Table 3.3 Characteristics of Scum 1 and Scum 2 to the digesters

Name Scum 1 Scum 2

Flowa

11 11

Total CODb

mgCOD/L 130,000 130,000

TKNb mgN/L 130 130

Total Pb

mgP/L 10 10

Nitrate Na mgN/L 0.2 0.2

pHa

7.5 7.5

Alkalinitya mmol/L 5 5

Inorganic S.Sb. mgISS/L 1 1

Calciuma mg/L 79 79

Magnesiuma mg/L 29 29

DOa mg/L 0 0

a The Regina WWTP, 2007;

b URS Corp., 2009.

3.2.2 Wastewater Fractions - BioWinTM

According to Dursun's research (Dursun et al., 2011), the results from the

sensitivity analysis revealed that gas production is sensitive to (1) the wastewater fraction

Fxsp (non-colloidal slowly biodegradable fraction of COD), and (2) the wastewater

fraction Fup (unbiodegradable particulate fraction of COD). In Parker's work, it was found

that a fraction of Fup and Fxsp affected anaerobic digestion performance for digesting

33

wasted activated sludge (Parker et al., 2008). Moreover, fraction Fna was adjusted during

steady-state simulation for the purpose of matching the plant’s measured data. The

wastewater fractions of raw influent are presented in Table 3.4.

Table 3.4 Raw influent (sewage) wastewater fractions (EnviroSim Associates Ltd.)

Element name Default Adjusted

Fbs - Readily biodegradable (including Acetate) [gCOD/g of total

COD] 0.16

Fac - Acetate [gCOD/g of readily biodegradable COD] 0.15

Fxsp - Non-colloidal slowly biodegradable [gCOD/g of slowly

degradable COD] 0.75 0.8

Fus - Unbiodegradable soluble [gCOD/g of total COD] 0.05

Fup - Unbiodegradable particulate [gCOD/g of total COD] 0.13 0.15

Fna - Ammonia [gNH3-N/gTKN] 0.66 0.63

Fnox - Particulate organic nitrogen [gN/g Organic N] 0.5

Fnus - Soluble unbiodegradable TKN [gN/gTKN] 0.02

FupN - N:COD ratio for unbiodegradable part. COD [gN/gCOD] 0.035

Fpo4 - Phosphate [gPO4-P/gTP] 0.5

FupP - P:COD ratio for unbiodegradable part. COD [gP/gCOD] 0.011

FZbh - Non-poly-P heterotrophs [gCOD/g of total COD] 0.0001

FZbm - Anoxic methanol utilizers [gCOD/g of total COD] 0.0001

FZaob - Ammonia oxidizers [gCOD/g of total COD] 0.0001

FZnob - Nitrite oxidizers [gCOD/g of total COD] 0.0001

FZamob - Anaerobic ammonia oxidizers [gCOD/g of total COD] 0.0001

FZbp - PAOs [gCOD/g of total COD] 0.0001

FZbpa - Propionic acetogens [gCOD/g of total COD] 0.0001

FZbam - Acetoclastic methanogens [gCOD/g of total COD] 0.0001

FZbhm - H2-utilizing methanogens [gCOD/g of total COD] 0.0001

34

3.2.3 Kinetic and Stoichiometric Parameters - BioWinTM

According to Dursun's research (Dursun et al., 2011), the results from the

sensitivity analysis show that gas production is sensitive to kinetic parameters, hydrolysis

rate, and hydrolysis half saturation constants. VFA in the digester is affected by the

kinetic parameters of the methanogens, namely, the acetolastic maximum rate coefficient,

and the acetoclastic decay rate.

Experience has shown that all stoichiometric and kinetic parameters do not

change dramatically for different systems treating municipal wastewater, and the default

values shown in Table 3.5 can be used directly (Melcer et al., 2003).

Table 3.5 Model kinetic parameters - Methanogens (EnviroSim Associates Ltd.)

Name Default Value

Acetoclastic Mu Max [1/d] 0.3 0.31

Acetoclastic decay rate [1/d] 0.13 0.11

Hydrolysis rate 0.1 0.1

Hydrolysis half saturation 0.15 0.15

3.3 Steady-state Calibration

Model calibration is defined as the adaptation of a model so that it fits a certain

set of data from the full-scale WWTP being studied (Petersen et al., 2003). Calibration

generally involves combining the "operational" or "controllable" aspects of the treatment

plant with the input wastewater characteristics and making adjustments to selected

parameters to fit a set of plant performance data (Melcer et al., 2003). Good calibration

requires knowledge of both model parameters and influent wastewater characteristics,

which have significant effects on the sludge digestion performance.

35

Steady-state simulation provides a solution to the system based on the flow-

weighted average influent loading to the system (EnviroSim Associates Ltd.). The

general approach to steady-state calibration of BioWinTM

is to initially fit the predicted

gas production generated from the model to the measured data. Once this is done,

subsequent refinements are made to the pH, VFA, and alkalinity predictions so that they

also correspond to the values measured at the plant. A step-by-step trial procedure is used

to adjust each parameter one at a time and to compare the model prediction to the

measured data.

Model Run #1. An initial model run was made using the average measured and

adjusted 2007 influent data from Table 3.2. Scum concentrations were taken from

Table 3.3. The splitter flow ratio of digester 1 to digester 2 was set to 1:1.

BioWinTM

default values were used for the wastewater fractions, kinetic and

stoichiometric parameters. Tentative conclusions drawn from the Model Run #1

are: (1) the BioWinTM

default values for wastewater fractions and kinetic

parameters have to be adjusted to better predict measured gas production,

alkalinity, and VFA, and (2) the adjusted COD value worked well.

Model Run #2. The second model run was made by increasing and decreasing the

raw influent Fxsp fraction stepwise in increments of 0.01 to values of 0.83 and 0.7,

respectively. When Fxsp = 0.8, the model predicted gas production was the best fit

to the measured values.

Model Run #3. The third model run was made by increasing and decreasing the

raw influent Fup fraction stepwise in increments of 0.01 to values of 0.16 and 0.1,

36

respectively, in order to improve gas predictions. A value of Fup = 0.15 was

chosen for model calibration.

Model Run #4. The forth model run was made by increasing and decreasing the

hydrolysis rate kinetic parameter stepwise in increments of 0.01 to values of 0.13

and 0.07, respectively. The default value of 0.1 was chosen because it yielded the

best gas predictions.

Model Run #5. The fifth model run was made by increasing and decreasing the

hydrolysis half saturation kinetic parameter in increments of 0.01 to values of

0.18 and 0.12, respectively, in order to better fit the gas prediction. However, like

the hydrolysis rate, a default value of 0.15 was chosen to be used.

Model Run #6. The sixth model run was made by increasing the Fbs fraction of the

scum stepwise in increments of 0.05 up to a maximum value of 0.80. A value of

0.75 was chosen because it best predicted gas production relative to the measured

values and adjusted values. However, using an Fbs fraction as high as 0.75,

resulted in very low FupN and FupP fractions. FupN = 5E10-10

and FupP = 5E10-10

were the selected values.

Model Run #7. The seventh model run was made by increasing and decreasing the

"Acetoclastic Mu Max - Methanogens" kinetic parameters stepwise in increments

of 0.01 to values of 0.27 and 0.33, respectively, to better predict VFA

concentrations relative to the measured values. A value of 0.31 of Acetoclastic

Mu Max was chosen.

Model Run #8. The eighth model run was made by increasing and decreasing the

acetoclastic decay rate stepwise in increments of 0.01 to values of 0.16 and 0.1

37

respectively. A value of 0.11 was chosen because it was the best at predicting

VFA concentrations relative to the measured values.

Model Run #9. The ninth model run was made by increasing and decreasing Fna

stepwise in increments of 0.01 to values of 0.61 and 0.71, respectively. Fna = 0.63

gave the best prediction of alkalinity relative to the measured values.

The calibration results of the digester effluent are presented in Table 3.6, and the

calibration results of the digester’s operation and performance are summarized in Table

3.7.

Table 3.6 Calibration - digesters effluent

Digester 1 Digester 2

Parameters Conc.

(mg/L)

Mass rate

(kg/d)

Conc.

(mg/L)

Mass rate

(kg/d)

Notes

Volatile suspended

solids

12,069.37 2,123.89 12,471.11 2,195.24

Total suspended solids 14,977.45 2,635.64 15,382.8 2,707.78

Particulate COD 19,108.97 3,362.68 19,744.3 3,475.52

Filtered COD 513.85 90.42 517.59 91.11

Total COD 19,622.83 3,453.10 20,261.9 3,566.63

Soluble PO4-P 168.46 29.64 161.56 28.44

Total P 379.67 66.81 379.68 66.83

Filtered TKN 362.47 63.78 346.62 61.01

Particulate TKN 637.82 112.24 656.47 115.56

Total Kjeldahl

Nitrogen

1,000.29 176.02 1,003.1 176.57

Filtered Carbonaceous

BOD

59.18 10.41 61.93 10.90

Total Carbonaceous

BOD

4,003.2 704.46 4,353.78 766.38

Nitrite + Nitrate 0 0 0 0

Total N 1,000.29 176.02 1,003.10 176.57

38

Total inorganic N 359.70 63.30 343.86 60.53

Alkalinity 38.10 6.71 37.18 6.54 mmol/L and

kmol/d

pH 6.68 6.68

Volatile fatty acids 82.33 14.49 86.18 15.17

Total precipitated

solids

0 0 0 0

Total inorganic

suspended solids

2,908.08 511.75 2,911.74 512.54

Ammonia N 359.70 63.30 343.86 60.53

Nitrate N 0 0 0 0

Table 3.7 Calibration - digesters operation and performance

Parameters Digester 1 Value Digester 2 Value Units

Hydraulic residence time 21.6 19.3 Days

Digester influent flow 175.97 176.03 m3 /d

Gas flow rate (dry) 3,129.23 3,061.25 m3 /d

Methane content 63.13 63.11 %

Carbon dioxide content 36.37 36.41 %

Hydrogen content 0.05 0.05 %

Ammonia content 0.34 0.32 %

VSS destruction 53.91 52.38 %

For comparison, the steady-state calibration results and the plant measured results

are shown in Table 3.8. Only important parameters which can indicate digester

performance are listed. Overall, steady-state calibration results of each parameter

matched the plant measured results. This indicated that the steady-state calibration was

successful.

39

Table 3.8 Calibration - steady-state simulation results vs. plant measured results

Calibration results Measurement

results

Normal range Match status

Digester 1 Digester 2 Digester 1 Digester 2

pH 6.68 6.68 6.78 6.8 6.6-7.6

(Technical Practice

Committee, 2012)

Acceptable

VFA 82.33

mg/L

86.18

mg/L

94.25

mg/L

96.375

mg/L

50 - 150 mg/L

(Technical Practice

Committee, 2012)

Acceptable

Alkalinity 38.1

mmol/L

37.18

mmol/L

32.53

mmol/L

35.7

mmol/L

40 - 100 mmol/L

(Tchobanoglous

et al., 2003)

Acceptable

Methane

content

63.13% 63.11% 62.2% 63.2% Excellent

Gas flow

rate

3,129

m3

3,061

m3

3,054

m3

2,404

m3

Acceptable

VSS

destruction

53.91% 52.38% 38-60 %

(Muller et al., 2011)

HRT 21.6

days

19.3

days

18days 18days 20 days

(Filbert, 2012)

Acceptable

3.4 Steady-state Validation

To check how the model fit to other data, a validation was carried out after the

calibration. Validation involves applying the calibrated model to a different set of

operating data than that used for calibration. As the average data from July 2007 were

used for calibration, the average data from August 2007 were used for validation. This

data was entered into each component of the BioWinTM

model to conduct a steady-state

validation. The validation results of the digester effluent and the results of the digester’s

operation and performance are show in Tables 3.9 and 3.10.

Table 3.9 Validation – digesters effluent

Digester 1 Digester 2

Parameters Conc.

(mg/L)

Mass rate

(kg/d)

Conc.

(mg/L)

Mass rate

(kg/d)

Notes

40

Volatile suspended solids 12,413.11 2,091.08 12,928.39 2,281.6

Total suspended solids 14,901.4 2,510.25 15,428.2 2,722.78

Particulate COD 19,657.49 3,311.45 20,473.6 3,613.19

Filtered COD 495.09 83.4 499.37 88.13

Total COD 20,152.58 3,394.85 20,973 3,701.32

Soluble PO4-P 167.68 28.25 160.76 28.37

Total P 383.13 64.54 384.59 67.87

Filtered TKN 355.83 59.94 339.72 59.95

Particulate TKN 651.08 109.68 673.55 118.87

Total Kjeldahl Nitrogen 1,006.92 169.62 1,013.27 178.82

Filtered Carbonaceous

BOD

62.32 10.5 66.48 11.73

Total Carbonaceous BOD 4,054.36 682.99 4,485.84 791.66

Nitrite + Nitrate 0 0 0 0

Total N 1,006.92 169.62 1,013.27 178.82

Total inorganic N 353.15 59.49 337.04 59.48

Alkalinity 36.32 6.12 35.32 6.23 mmol/L

and kmol/d

pH 6.66 6.66

Volatile fatty acids 86.7 14.61 92.53 16.33

Total precipitated solids 0 0 0 0

Total inorganic

suspended solids

2,488.3 419.17 2,499.86 441.18

Ammonia N 353.15 59.49 337.04 59.48

Nitrate N 0 0 0 0

Table 3.10 Validation - digesters operation and performance

Parameters Digester 1 Value Digester 2 Value Units

Hydraulic residence

time

22.2 19.7 Days

Digester influent flow 168.46 176.48 m3 /d

Gas flow rate (dry) 3,206.06 3,186 m3 /d

Methane content 62.98 62.98 %

41

Carbon dioxide content 36.59 36.62 %

Hydrogen content 0.05 0.04 %

Ammonia content 0.31 0.29 %

VSS destruction 55.55 53.71 %

For comparison, the steady-state validation results and the plant measured results

are shown in Table 3.11. Only important parameters which indicate digester performance

are listed. The close match between the model and the data indicates that the steady-state

validation was successful.

Table 3.11 Validation - steady-state simulation results vs. plant measured results

Validation results Measurement results Normal range Match

status Digester 1 Digester 2 Digester 1 Digester 2

pH 6.66 6.66 6.84 6.86 6.6-7.6

(Technical Practice

Committee, 2012)

Acceptable

VFA 86.70

mg/L

92.53

mg/L

98.43

mg/L

99.43

mg/L

50 - 150 mg/L

(Technical Practice

Committee, 2012)

Acceptable

Alkalinity 36.32

mmol/L

35.32

mmol/L

33.18

mmol/L

36.06

mmol/L

40 - 100 mmol/L

(Tchobanoglous

et al., 2003)

Acceptable

Methane

content

62.98% 62.98% 62.25% 63.5% Excellent

Gas flow

rate

3,206

m3

3,186

m3

3,066

m3

2,349 m3 Acceptable

VSS

destruction

55.55% 53.71% 38-60 %

(Muller et al., 2011)

HRT 22.2 days 19.7 days 18 days 18 days 20 days

(Filbert, 2012)

Acceptable

3.5 Dynamic Calibration

Dynamic simulation shows the time-varying system response based on the time-

varying influent loading to the system (EnviroSim Associates Ltd.). The dynamic

calibration was run for 30 days. The influent characteristics are presented in Tables A-1,

A-2, and A-3.

42

A selection of diagrams showing gas flow rate, pH, VFA, and alkalinity are shown in

Figures 3.2 to 3.9, respectively, to demonstrate the comparison of the dynamic simulation

results and the plant measured results. The conclusions drawn from Figures 3.2 to 3.9 are

as follows:

Figure 3.2 shows the gas flow rate profiles. It demonstrates that the model

predicted values (solid lines) fit the measured values (squares) for digester 1.

Figure 3.3 indicates that the model predicted values for digester 2 are higher than

the measured values.

Figure 3.4 and 3.5 show that the model predicted pH values are lower than the

measured values.

Figure 3.6 and 3.7 show that the measured alkalinity values and model predicted

values are in good agreement with one another.

Though available measured VFA values are limited, the Figure 3.8 and 3.9 show

that the measured values and model predicted values are in close agreement.

The predicted values generated from the model and the measured data show a

relatively close fit. It was not possible to get all model outputs to correspond exactly to

the plant measurements. The measurements had high variability, but model correlation

with “reasonable accuracy” was achieved which indicated that the dynamic calibration

was successful.

43

Figure 3.2 Calibration - Digester 1 gas flow rate

Figure 3.3 Calibration - Digester 2 gas flow rate

Digester 1 Gas Flow Rate

Digester 1 Gas flow rate (dry) "Digester 1 Gas flow rate (actual)"

DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

4,400

4,200

4,000

3,800

3,600

3,400

3,200

3,000

2,800

2,600

2,400

2,200

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0



DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

4,200

4,000

3,800

3,600

3,400

3,200

3,000

2,800

2,600

2,400

2,200

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

44

Figure 3.4 Calibration - Digester 1 pH

Figure 3.5 Calibration - Digester 2 pH

Figure 3.6 Calibration - Digester 1 alkalinity

Figure 3.7 Calibration - Digester 2 alkalinity

Digester 1 pH

Digester 1 pH "Digester 1 pH (actual)"

DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

pH

6.8

6.75

6.7

6.65

6.6

Digester 2 pH


DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

pH

6.8

6.75

6.7

6.65

6.6

Digester 1 Alkalinity

Digester 1 Alkalinity "Digester 1 ALK (actual)"

DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0



DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

45

Figure 3.8 Calibration - Digester 1 VFA

Figure 3.9 Calibration - Digester 2 VFA

3.6 Dynamic Validation

To validate the dynamic calibration, dynamic validation was conducted using the

influent characteristics presented in Table B-1, B-2 and B-3. A selection of plots showing

gas flow rate, pH, VFA, and alkalinity are shown in Figures 3.10 to 3.17, respectively.

The conclusions drawn from the analyses of the dynamic validation figures are:

From Figure 3.10 and 3.11, the model predicted gas flow rate values for digester 1

(solid lines) are fit to the measured values (squares) for digester 1. But for

digester 2, the model predicted values are higher than the measured values.

Figure 3.12 and 3.13 present the measured alkalinity values and model predicted

values for both digesters are in good agreement.

Though available measured data for VFA are limited, the Figure 3.14 and 3.15

show that the measured values and predicted values are in close agreement.

Digester 1 VFA

Digester 1 Volatile fatty acids "Digester 1 VFA (actual)"

DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

CO

NC

. (m

g/L

)

140

120

100

80

60

40

20

0

Digester 2 VFA


DATE

7/31/20077/29/20077/27/20077/25/20077/23/20077/21/20077/19/20077/17/20077/15/20077/13/20077/11/20077/9/20077/7/20077/5/20077/3/20077/1/2007

CO

NC

. (m

g/L

)

140

120

100

80

60

40

20

0

46

The model predicted pH values (Figure 3.16 and 3.17) are lower than the

measured values.

These observations confirm that the dynamic validation was successful.

Figure 3.10 Validation - Digester 1 gas flow rate

Figure 3.11 Validation - Digester 2 gas flow rate

Figure 3.12 Validation - Digester 1 alkalinity



DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0



DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0



DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

47

Figure 3.13 Validation - Digester 2 alkalinity

Figure 3.14 Validation - Digester 1 VFA

Figure 3.15 Validation - Digester 2 VFA

Figure 3.16 Validation - Digester 1 pH


Digester 2 Alkalinity "Digester 2 Alkalinity (actual)"

DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

Digester 1 VFA


DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007

CO

NC

. (m

g/L

)

160

140

120

100

80

60

40

20

0

Digester 2 VFA


DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007

CO

NC

. (m

g/L

)

160

140

120

100

80

60

40

20

0

Digester 1 pH


DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007

pH

7

6.95

6.9

6.85

6.8

6.75

6.7

6.65

6.6

6.55

6.5

48

Figure 3.17 Validation - Digester 2 pH

4.0 RESULTS AND DISCUSSION

This section presents a dynamic simulation of the startup of an anaerobic digester

fed with primary sludge. The results of the dynamic simulation were also used to develop

general strategies that can be used to assist in the startup of anaerobic digesters at other

WWTPs.

4.1 Dynamic Simulation for the Startup of an Anaerobic Digester

Dynamic simulations presented in this section were conducted using the data

collected from digester 2 at the Regina WWTP. This is because digester 1 at the Regina

WWTP was out of service for cleaning during the time period in which data was

available. Digester 2 was cleaned in the winter of 2010 and it was ready for startup in

early spring. It became stable late fall. Thus, the dynamic simulations presented here

encompass the period from April 16, 2012 when sludge seed was first introduced to the

digester to September 30, 2012 when the maximum feed rate of 300 m3 of primary sludge

was reached.

The startup procedure consists of 10 well-defined steps:

1. Filling: the digester was filled to 1.0 m below normal water level with a mixture

of primary effluent and tertiary effluent to minimize the presence of DO, high

Digester 2 pH


DATE

9/1/20078/30/20078/28/20078/26/20078/24/20078/22/20078/20/20078/18/20078/16/20078/14/20078/12/20078/10/20078/8/20078/6/20078/4/20078/2/2007

pH

7

6.95

6.9

6.85

6.8

6.75

6.7

6.65

6.6

6.55

6.5

49

COD, sulfate, and toxins that could be potentially harmful to the anaerobic

digester seed.

2. Pressure test biogas piping: nitrogen gas was introduced downstream of the gas

compressor to purge and pressure test the biogas piping before the roof access

was sealed. This was done to ensure that sections of piping removed for

maintenance were completely sealed.

3. Pressure test gas head space: the digester’s roof access hatches were closed and

natural gas was introduced through the vacuum relief line to increase the digester

pressure to 125 mm H2O. The digester was then pressure tested by raising the

water level in the digester by 40 mm, which increased the digester gas pressure to

500 mm H2O. Soap tests were conducted on all threaded or flanged connections

on the digester.

4. Heating: the digester heating system was started and the temperature was set to

35 °C.

5. Dilution of potential explosive gas mixture: biogas from digester 1 was

transferred to digester 2 to ensure that any potential pockets of explosive gas were

diluted. Biogas from digester 1 had a methane concentration of 62%. After mixing

with air contained in digester 2, the methane concentration was reduced to 30%,

which is well above the maximum explosive limit.

6. Mixing: to attain uniform temperatures in the digester, a gas recirculation

compressor was started after it was confirmed that the biogas mixture was above

the maximum explosive limit.

50

7. Seeding: the digester was seeded with 40 m3 of anaerobically digested sludge

hauled from the City of Saskatoon WWTP. This volume of seed was selected

based on transportation costs, which were about $4,000 per 40 m3 of sludge.

8. Feeding: digester 2 was initially fed 100 kg of primary sludge. The feed rate was

progressively increased as presented in Table E-1 and E-2 in the Appendix E.

9. Alkalinity: sodium bicarbonate was used to adjust the digester pH to 6.8 to 7.2.

Bicarbonate was added in the event that the digester pH decreased to 6.3.

10. Monitoring: digester startup processes were monitored by sampling the digester

twice daily at 7:30 AM and 2:00 PM for the analysis of the following parameters:

a. Volatile acids – A range of operation between 100 to 300 mg/L was set for

the startup. If the VA exceeded 300 mg/L, the sludge feed would need to

be reduced or stopped entirely.

b. Total alkalinity – A range of operation between 1000 to 2000 mg/L (20 to

40 mmol/L) was set for the startup.

c. pH – A range of operation between 6.5 and 7.0 was set for the startup. If

the pH decreased below 6.5, the sludge feed would need to be reduced or

stopped entirely. If the pH decrease below 6.3, sodium bicarbonate would

need to be added.

d. TS – Total solids was used as an indicator of the biomass production (TS

= TSS (total suspended solids) + TDS (total dissolved solids)).

e. Gas production and gas composition were also used as an indicator of the

overall progress of the digester startup process.

51

4.1.1 Model Configuration

The BioWinTM

model configuration is shown in Figure 4.1. It includes the

following influent streams: a raw influent element, a grit tank element, a sedimentation

tank element, scum 1 and scum 2 elements, and new bicarbonate and seed elements.

Input parameters for these streams are given in Appendix D. It should be noted that on

Day 1 the bicarbonate stream was given the values of a primary effluent/tertiary effluent

(Tables C-4 and C-5 in the Appendix C) mixture to minimize the presence of DO, high

COD, sulfate, and toxins that could potentially harm the anaerobic digester seed. The

primary effluent/tertiary effluent was taken from the whole plant steady-state simulation

model (Appendix C).

Figure 4.1 Configuration of the BioWinTM

model for anaerobic digestion startup

4.1.2 Dynamic Simulation

A dynamic startup simulation can be conducted by giving a fit status of the model

predicted values to the actual measured values (base case). A selection of plots from the

dynamic simulation are shown in Figures 4.2 to 4.7.

Influent

Digester 1

Grit Tank

Digester 2

Primary Effluent to Lagoons

Cake

GritScum 1

Scum 2

PS to lagoons

Bicarbonate Seed

52

Figure 4. 2 Actual startup simulation - Digester 2 VFA

Figure 4.3 Actual startup simulation - Digester 2 pH

Figure 4.4 Actual startup simulation - Digester 2 gas flow rate

VFA


9/2

9/2

01

2

9/2

2/2

01

2

9/1

5/2

01

2

9/8

/20

12

9/1

/20

12

8/2

5/2

01

2

8/1

8/2

01

2

8/1

1/2

01

2

8/4

/20

12

7/2

8/2

01

2

7/2

1/2

01

2

7/1

4/2

01

2

7/7

/20

12

6/3

0/2

01

2

6/2

3/2

01

2

6/1

6/2

01

2

6/9

/20

12

6/2

/20

12

5/2

6/2

01

2

5/1

9/2

01

2

5/1

2/2

01

2

5/5

/20

12

4/2

8/2

01

2

4/2

1/2

01

2

4/1

4/2

01

2

4/7

/20

12

CO

NC

. (m

g/L

)

700

600

500

400

300

200

100

0

Digester 2 pH


9/2

9/2

01

2

9/2

2/2

01

2

9/1

5/2

01

2

9/8

/20

12

9/1

/20

12

8/2

5/2

01

2

8/1

8/2

01

2

8/1

1/2

01

2

8/4

/20

12

7/2

8/2

01

2

7/2

1/2

01

2

7/1

4/2

01

2

7/7

/20

12

6/3

0/2

01

2

6/2

3/2

01

2

6/1

6/2

01

2

6/9

/20

12

6/2

/20

12

5/2

6/2

01

2

5/1

9/2

01

2

5/1

2/2

01

2

5/5

/20

12

4/2

8/2

01

2

4/2

1/2

01

2

4/1

4/2

01

2

4/7

/20

12

pH

7

6

5

Digester 2 gas flow rate


9/2

9/2

01

2

9/2

2/2

01

2

9/1

5/2

01

2

9/8

/20

12

9/1

/20

12

8/2

5/2

01

2

8/1

8/2

01

2

8/1

1/2

01

2

8/4

/20

12

7/2

8/2

01

2

7/2

1/2

01

2

7/1

4/2

01

2

7/7

/20

12

6/3

0/2

01

2

6/2

3/2

01

2

6/1

6/2

01

2

6/9

/20

12

6/2

/20

12

5/2

6/2

01

2

5/1

9/2

01

2

5/1

2/2

01

2

5/5

/20

12

4/2

8/2

01

2

4/2

1/2

01

2

4/1

4/2

01

2

4/7

/20

12

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

53

Figure 4.5 Actual startup simulation - Digester 2 alkalinity

Figure 4.6 Actual startup simulation - Digester 2 TSS

Figure 4.7 Actual startup simulation - Digester 2 methane content

Alkalinity

Digester 2 Alkalinity "Digester 2 Alkalinity(actual)"

9/2

9/2

01

2

9/2

2/2

01

2

9/1

5/2

01

2

9/8

/20

12

9/1

/20

12

8/2

5/2

01

2

8/1

8/2

01

2

8/1

1/2

01

2

8/4

/20

12

7/2

8/2

01

2

7/2

1/2

01

2

7/1

4/2

01

2

7/7

/20

12

6/3

0/2

01

2

6/2

3/2

01

2

6/1

6/2

01

2

6/9

/20

12

6/2

/20

12

5/2

6/2

01

2

5/1

9/2

01

2

5/1

2/2

01

2

5/5

/20

12

4/2

8/2

01

2

4/2

1/2

01

2

4/1

4/2

01

2

4/7

/20

12

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

TSS

Digester 2 Total suspended solids TS (actual)

9/2

9/2

01

2

9/2

2/2

01

2

9/1

5/2

01

2

9/8

/20

12

9/1

/20

12

8/2

5/2

01

2

8/1

8/2

01

2

8/1

1/2

01

2

8/4

/20

12

7/2

8/2

01

2

7/2

1/2

01

2

7/1

4/2

01

2

7/7

/20

12

6/3

0/2

01

2

6/2

3/2

01

2

6/1

6/2

01

2

6/9

/20

12

6/2

/20

12

5/2

6/2

01

2

5/1

9/2

01

2

5/1

2/2

01

2

5/5

/20

12

4/2

8/2

01

2

4/2

1/2

01

2

4/1

4/2

01

2

4/7

/20

12

CO

NC

. (m

gT

SS

/L)

24,00024,000

22,00022,000

20,00020,000

18,00018,000

16,00016,000

14,00014,000

12,00012,000

10,00010,000

8,0008,000

6,0006,000

4,0004,000

2,0002,000

00

Methane content

Digester 2 Methane content "Digester 1 methane content (actual)"9

/29

/20

12

9/2

2/2

01

2

9/1

5/2

01

2

9/8

/20

12

9/1

/20

12

8/2

5/2

01

2

8/1

8/2

01

2

8/1

1/2

01

2

8/4

/20

12

7/2

8/2

01

2

7/2

1/2

01

2

7/1

4/2

01

2

7/7

/20

12

6/3

0/2

01

2

6/2

3/2

01

2

6/1

6/2

01

2

6/9

/20

12

6/2

/20

12

5/2

6/2

01

2

5/1

9/2

01

2

5/1

2/2

01

2

5/5

/20

12

4/2

8/2

01

2

4/2

1/2

01

2

4/1

4/2

01

2

4/7

/20

12

ME

TH

AN

E C

ON

TE

NT

(%

)

100

90

80

70

60

50

40

30

20

10

0

54

From the data presented in Figures 4.2 – 4.7, the following conclusions were

drawn:

Figure 4.2 and Figure 4.3 indicate that increasing VFA leads to a decrease in pH,

which is an indicator of startup performance problems.

Figure 4.2 indicates that the measured VFA values and model predicted values are

in good agreement before scum was added. However, during April 28 to May 12,

the values of VFA exceeded 300 mg/L for both of model prediction and the base

case. This would make the digester nonfunctional and cause the startup to fail.

Figure 4.3 shows that the model predicted values for pH are lower than the

measured values. Model predicted values overestimate the drop in pH between

April 21 and May 19.

Figure 4.4 presents the model predicted biogas flow rate in close agreement with

the measured values up to August 28. On August 28, scum was added to the

digester. After the scum addition, the gas prediction was lower than the measured

values. The scum provided additional substrate for digestion which promoted

greater gas production. This change in substrate addition was not accounted for in

the model predicted values.

Figure 4.5 shows that the measured alkalinity values were higher than the model

predicted values.

Figure 4.7 depicts a good fit between the predicted methane content and the

measured values. On April 14, the plant measured a high methane content value

because additional methane was introduced into the digester to avoid explosive

55

dilute gases. After April 16 seed sludge was transferred to the digester, there was

a linear increase in methane content up to June 23, when the anaerobic digester

stabilized. At this point the methane content remained at a plateau for both the

model prediction and the measured values.

In summary, the values predicted from the dynamic simulation fit very well to the

measured values, especially for the following parameters: VFA, gas flow rate, alkalinity,

TSS, and methane content. The values predicted for pH did not fit well to the measured

values during the first 30 days of the dynamic simulation. Therefore, VFA was used as

the major criteria to monitor the anaerobic digester performance, along with gas flow

rate, alkalinity, and TSS.

4.2 Optimization of Startup

Three strategies have been developed for a timely and cost-effective startup of an

anaerobic digester based on a simulation of the BioWinTM

model. From these strategies,

the optimal sludge feed rate, the optimal seed sludge amount, and the optimal bicarbonate

concentration can be determined under different startup conditions.

4.2.1 Optimization of Sludge Feed Rate

Sludge feed rate is the most important parameter for the successful startup of an

anaerobic digester. A proper sludge feed rate will prevent the digester from becoming

acidic and failing to start up. The model simulation has identified that the best way to

feed raw sludge during the startup procedures is to feed the sludge into the digester at a

gradually increasing feed rate. In this research, the sludge feed rate strategies were

developed based on a seed volume of 120 m3.

56

The first strategy developed for the sludge feed rate was called F/M proportion

(Figure 4.8) which was calculated based on the feed to mass ratio (F/M ratio). The second

strategy developed for the sludge feed rate was called flow proportion which was

calculated based on increasing percentages of digester capacity. This is shown in Figure

4.9. The digester sludge feed rate was limited to values less than 300 m3/d based on a

SRT of 12 days at 35 °C. A lower SRT limit of 10 days may wash out a portion of the

menthanogenic population, while a higher SRT (over 20 days) may dilute the contents of

the digester.

Figure 4.8 Sludge feed rate calculated on F/M proportion

0

50

100

150

200

250

300

17-A

pr

24-A

pr

1-M

ay

8-M

ay

15-M

ay

22-M

ay

29-M

ay

5-J

un

12

-Jun

19

-Jun

26

-Jun

3-J

ul

10-J

ul

17-J

ul

24-J

ul

31-J

ul

7-A

ug

14-A

ug

21-A

ug

28-A

ug

4-S

ep

11-S

ep

18-S

ep

25-S

ep

2-O

ct

Slu

dge

m3

Sludge feed rate F/M proportion m3/d

Y = 0.008X3 - 0.0262X

2 +0.6537X + 7.6413

R2 = 0.9984

57

Figure 4.9 Sludge feed rate calculated on percent of digester volume proportion

Table 4.1 shows six model dynamic simulations conducted under different

situations:

Table 4.1 Situation of each model dynamic simulation for optimization of startup

Dynamic

simulation

Seed volume Sludge feed rate Sodium

bicarbonate

Scum

Base case 40 m3 Slow: actual plant

feed rate

Added Added

Run #1 120 m3 F/M proportion No No

Run #2 80 m3 F/M proportion No No

Run #3 40 m3 F/M proportion Yes No

Run #4 120 m3 Flow proportion No No

Run #5 80 m3 Flow proportion No No

Run #6 40 m3 Flow proportion Yes No

The base case is the actual plant startup of the anaerobic digester. It is based on a

seed volume of 40 m3 and a slow feed rate with bicarbonate addition to control the pH

0 25 50 75

100 125 150 175 200 225 250 275 300

17-A

pr

24-A

pr

1-M

ay

8-M

ay

15-M

ay

22-M

ay

29-M

ay

5-J

un

12-J

un

19-J

un

26-J

un

3-J

ul

10-J

ul

17-J

ul

24-J

ul

31-J

ul

7-A

ug

14-A

ug

21-A

ug

28-A

ug

4-S

ep

11

-Sep

18-S

ep

25-S

ep

2-O

ct

Ssl

udge

m3

Sludge feed rate ( percent of digester volume proportion) m3/d

Y1 = 0.751X + 1.7273

Y2 = 7.6X - 311.6

58

and the digester performance. In addition, scum was added on August 28 to increase

biogas production.

Dynamic simulation runs from #1 to #6 are the trials for the optimization of the

startup:

Run #1 was conducted on a seed volume of 120 m3 with an F/M proportioned

sludge feed rate. No bicarbonate or scum was added. A selection of plots showing

VFA, gas flow rate, alkalinity, TSS, and pH of the dynamic simulation results are

shown as follows:

Figure 4.10 Run #1 simulation results of VFA

From Figure 4.10, the following conclusions can be made:

After April 16, seed was transferred into the digester. This resulted in a linear

increase in the VFA predicted by Run #1. On April 27, the VFA reached a peak

value of 175 mg/L. This value is in the range of 100 mg/L to 300 mg/L and even

lower than 200 mg/L. This provides very good startup performance.

After April 27, the value of VFA (Run #1) decreased to a value between 80 mg/L

and 200 mg/L for the remainder of the run. This value of VFA is optimal for

biogas production.

Digester VFA

Run # 1 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

g/L

)

600

500

400

300

200

100

0

59

Figure 4.11 Run #1 simulation results of gas flow rate


After the seed transfer on April 16, it took 79 days (April 16 to July 4) for Run #1

to obtain over 2000 m3/d of biogas production based on the F/M proportioned

sludge feed rate. The maximum feed volume reached was 275 m3.

After July 4, the feed rate was maintained at a constant maximum rate of 275 m3.

Soon after, a maximum biogas production of 3400 m3/d was achieved (Run #1).

However, following this peak, the biogas production suddenly decreased. This

was most likely caused by excessive feed which led to acid buildup and death for

a significant portion of the methanogenic population. The loss of methanogens

reduced the amount of sludge digested, which in turn reduced biogas production.

However, the methanogenic population can recover during operation. On July 20,

the biogas production increased to 3000 m3/d and then stabilized over the

remainder of the run. This indicated at least partial recovery and stabilization of

the methanogenic population.


Run # 1 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

60

Figure 4.12 Run #1 simulation results of alkalinity


From the beginning to the end of Run #1, alkalinity gradually increased up to

July 30, when it began to level off and stabilize.

Because no bicarbonate was added during Run #1, the bicarbonate value did not

suddenly increase in concentration on May 18 like the base case.

Likewise, there was no scum addition after August 28 during Run #1. For this

reason, the predicted alkalinity values are lower than the base case.

Figure 4.13 Run #1 simulation results of TSS


Run # 1 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

Digester 2 TSS

Run # 1 Base case1

0/1

/20

12

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

gT

SS

/L)

22,000

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

61


Total suspended solids can be used as an indicator of biomass production. The

decrease in TSS observed in Run #1 during July 12 to July 20 was caused by the

death of a significant portion of the methanogenic population. The result of this

die-off, directly impacted biomass concentration and TSS. However, data

observed on later dates indicate recovery and stabilization of the methanogenic

population.

The trends observed in the model prediction of TSS are similar to the trends

observed in the model prediction of biogas generation. This is because the amount

of biomass in the digester directly affects biogas production.

Overall, Run #1 of the dynamic simulation, with a seed volume of 120 m3 and an

optimized sludge feed rate (based on F/M), provided very good startup performance

relative to the base case.

Run #4 was conducted on a seed volume of 120 m3 with a flow proportioned

sludge feed rate. No bicarbonate or scum was added during this run. The dynamic

simulation results are shown in Figures 4.14 to 4.17.


Digester VFA

Run # 4 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

g/L

)

600

500

400

300

200

100

0

62

It was found from Figure 4.14 that the predicted values for VFA at the beginning

of startup were around 120 mg/L. This ensured a very well controlled startup

performance. The values remained within the range of 80 mg/L and 200 mg/L for the

remainder of the run. These values are optimal for providing maximum biogas

production.


Figure 4.15 resembles the biogas production predicted during Run #1. After the

seed transfer on April 16, it took 76 days (April 16 to July 1) for Run #4 to obtain 2000

m3/d of biogas production based on the flow proportioned sludge feed rate. The

maximum volume of feed reached was 266 m3. After July 1, the feed rate was maintained

constant at the maximum rate, and the digester soon achieved its maximum biogas

production of 3400 m3/d (Run #4). However, the biogas production suddenly decreased

due to over feeding which produced excessive acid accumulation and a reduction in the


Run # 4 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

63

methanogenic population. The methanogenic population recovered within one month,

and on July 20, biogas production increased to 3000 m3/d and then became stable.


From Figure 4.16, it was observed that alkalinity gradually increased up to July

30. After this time, the values of Run #4 stabilized. There was no scum addition after

August 28 during Run #4. As a result, the alkalinity values were lower during this time

compared to the base case.



Run # 4 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

Digester 2 TSS

Run # 4 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

gT

SS

/L)

22,000

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

64

From Figure 4.17, it was found that the reduction in TSS during Run #4, from

July 12 to July 20, was caused by the death of a significant portion of the methanogenic

population. The result of this die-off was a reduction in the amount of biomass observed

in the digester. The trend for the model predicted TSS values are similar to the trend of

the predicted biogas values. This is because the amount of biomass in the digester affects

the amount of biogas produced.

From Run #1 and Run #4, it can be found that a seed volume of 120 m3, with the

two developed sludge feed rate strategies, provided ideal startup performance. As such, it

was not necessary to conduct a dynamic simulation for seed volumes higher than 120 m3,

since high seed volumes are undesirable.

The following conclusions are drawn from the dynamic simulation Run #1 and

Run #4:

1. Both sludge feed rate (F/M proportioned and flow proportioned) provided good

startup performance.

2. An equation was developed for to calculate the sludge feed rate during the startup

period for a sludge seed volume of 120 m3. The feed rate in this equation is

proportionate to F/M:

Y = 0.008X3 - 0.0262X

2 +0.6537X + 7.6413, R

2 = 0.9984 (4.1)

The sludge feed rate cannot exceed maximum sludge feed rate (

to

prevent washout of methanogenic bacteria. The SRTmin defined in this study is 12

days.

Equation 4.2 describes the food to mass regression (F/Mregression) ratio that took

into consideration food to mass for feeding which was developed based on the

65

Regina WWTP. This equation was used to calculate the sludge feed rate further to

develop Equation 4.1.

Y = 0.0549X2 - 8.0382X + 329, R

2 = 1 (4.2)

3. The flow proportioned feed rate during startup at the Regina WWTP can be

determined by two equations:

Y1 = 0.751X + 1.7273 (4.3)

Y2 = 7.6X - 311.6 (4.4)

As such, the sludge feed rate cannot exceed maximum sludge feed rate that is

. The SRTmin defined in this study is 12 days.

4. For WWTPs transferring seed sludge from adjacent digesters, the minimum

amount of seed sludge that would allow the maximum feed rate is about half of

the normal total solids concentration in the digester. This means that the required

amount of seed sludge is 50% of the digester capacity.

4.2.2 Optimization of Seed Sludge

The amount of initial seed sludge affects the digester startup performance. In this

research, different initial seed volumes were selected with different sludge feed rates to

develop a correlation between the amount of seed volume versus sludge feed rate. The

F/M proportioned and flow proportioned sludge feed rates were developed based on a

seed volume of 120 m3. But a seed volume 40 m

3 and 80 m

3 were also used in dynamic

simulations with the two developed sludge feed rate strategies for the purpose of

selecting the most cost-effective seed volume. The results of using a seed volume of 120

m3 have been presented above. The follow section will present the dynamic simulation

results when a seed volume of 80 m3 and 40 m

3 are used.

66

Run #2 was conducted using a seed volume of 80 m3 with a F/M proportioned


simulation results are shown in Figure 4.18 to 4.21.


From Figure 4.18, it can be seen that the VFA values during the first startup

period (April 16 to May 7) reached a peak value of 240 mg/L. However, the VFA

concentration did not exceed the VFA maximum value of 300 mg/L. Therefore, a seed

volume of 80 m3 with an F/M proportioned sludge feed rate can be implemented during

digester startup. After May 7, the VFA values remained between 80 mg/L to 180 mg/L

which yielded high amounts of biogas.

Figure 4.19 presents the gas flow rate over time. The amount of biogas predicted

closely resembles Run #1, because the feed rate is the same and the VFA concentrations

were within the optimal operating range. From Figure 4.20 and Figure 4.21, it was found

that the predicted alkalinity and TSS values had no significant deviations from the results

generated in Run #1.

Therefore, the F/M proportioned sludge feed rate developed on a seed volume of

120 m3 closely resembles the dynamic simulation using a seed volume of 80 m

3.

Digester VFA

Run # 2 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

g/L

)

600

500

400

300

200

100

0

67





Run # 2 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0


Run # 2 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

Digester 2 TSS

Run # 2 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

gT

SS

/L)

20,000

15,000

10,000

5,000

68

Run #3 was conducted on a seed volume of 40 m3 with an F/M proportioned

sludge feed rate. Bicarbonate was added but scum was not added. The dynamic

simulation results are shown in Figures 4.22 to 4.26:



An additional run using the same parameters as Run #3 was conducted, but

without the addition of bicarbonate. In this run, the VFA values increased linearly to

Digester VFA

Run # 3 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

g/L

)

600

500

400

300

200

100

0


Run # 3 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

69

4000 mg/L before plateauing. The pH values decreased to below 5.0 and did not recover.

These predicted values were far beyond the ranges required for a successful startup.

Therefore, the bicarbonate addition was needed for optimal performance.

With the addition of bicarbonate, Figure 4.22 shows that during the first startup

period (April 16 to May 7), a maximum VFA value of 320 mg/L was reached on April

30. This value exceeds the maximum value of 300 mg/L. A VFA concentration in excess

of 300 mg/L requires a reduction or stoppage in the sludge feed rate. Although

bicarbonates had been added and the alkalinity and pH values were optimized, from April

25 to May 14 (Figure 4.24 and 4.26), the alkalinity and pH rapidly increased. The

addition of bicarbonate cannot be used to bring the VFA value under 300 mg/L. Finally,

the F/M proportioned sludge feed rate developed using a seed volume of 120 m3 does not

fit with the values predicted using a seed volume of 40 m3. Thus, a slower and smaller

sludge feed rate was required for a seed volume of 40 m3. This results in a longer startup

time for a seed volume of 40 m3 compared to a seed volume of 80 m

3 or 120 m

3.



Run # 3 Base case1

0/1

/20

12

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

70


Figure 4.26 Run #3 simulation results of pH



simulation results are shown in Figures 4.27 to 4.30:


Digester 2 TSS

Run # 3 TS (actual)

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

gT

SS

/L)

20,000

15,000

10,000

5,000

0

Digester 2 pH

Run # 3 Base case

DATE

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

pH

7.0

6.0

Digester 2 VFA

Run # 5 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

g/L

)

600

500

400

300

200

100

0

71

From Figure 4.27, it can be seen that the VFA predicted values during the first

startup period (April 16 to May 7) achieved 200 mg/L. During the remainder of the run,

the VFA concentration did not exceed the VFA maximum value of 300 mg/L. Therefore,

a seed volume of 80 m3 with a flow proportioned sludge feed rate can yield a successful

digester startup. After May 7, the VFA values remained between 80 to 180 mg/L and

high amounts of biogas were predicted. Figure 4.19 presents the gas flow rate. The

amount of biogas production closely resembles Run #4, because it possesses the same

sludge feed rate and similar VFA values. From Figure 4.20 and Figure 4.21, it was found

that the model prediction alkalinity and TSS values had no significant deviations from the

values predicted during Run #4.

Therefore, the flow proportioned sludge feed rate developed on a seed volume of

120 m3 is in agreement with the values predicted using a seed volume of 80 m

3.



Run # 5 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

8/6

/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

72




sludge feed rate. Bicarbonate was added but scum was not added. The dynamic

simulation results are shown in Figures 4.31 to 4.35.

An additional run using the same parameters as Run #6 was conducted, but

without the addition of bicarbonate. In this run, the VFA values increased linearly to

4000 mg/L before plateauing. The pH values decreased to below 5.0 and did not recover.

These predicted values were far beyond the ranges required for a successful startup.

Therefore, bicarbonate addition was needed for optimal performance.


Run # 5 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

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8/2

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01

2

8/2

0/2

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/20

12

7/3

0/2

01

2

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3/2

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7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

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2

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/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

Digester 2 TSS

Run # 5 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

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/20

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8/2

7/2

01

2

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/20

12

7/3

0/2

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2

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3/2

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2

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01

2

7/9

/20

12

7/2

/20

12

6/2

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01

2

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2

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1/2

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2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

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01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

gT

SS

/L)

22,000

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

73




Digester 2 VFA

Run # 6 Base case

10

/1/2

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2

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/20

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/20

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7/2

/20

12

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2

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01

2

6/4

/20

12

5/2

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01

2

5/2

1/2

01

2

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01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

g/L

)

600

500

400

300

200

100

0


Run # 6 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

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/20

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/20

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/20

12

7/2

/20

12

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1/2

01

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6/4

/20

12

5/2

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01

2

5/2

1/2

01

2

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2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

GA

S F

LO

W R

AT

E (

DR

Y)

(m3

/d)

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0


Run # 6 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

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2

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/20

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01

2

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/20

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7/3

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2

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/20

12

7/2

/20

12

6/2

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01

2

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2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

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01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

mo

l/L

)

45

40

35

30

25

20

15

10

5

0

74

With the addition of bicarbonate, Figure 4.31 shows that during the first startup

period (April 16 to May 7), a maximum VFA value of 360 mg/L was obtained. This

value exceeded the maximum value of 300 mg/L, meaning that a reduction or stoppage in

sludge feed was needed. Although bicarbonates had been added to optimize the alkalinity

and pH values, from April 28 to May 17 (Figure 4.33 and 4.35), the alkalinity and pH

values rapidly increased. The biogas and TSS predicted values matched well to the actual

measured values. It was determined that the addition of bicarbonate could not reduce the

VFA value below 300 mg/L. Finally, the flow proportioned sludge feed rate developed

using a seed volume of 120 m3 does not fit with the values predicted using a seed volume

of 40 m3. Thus, a slower and smaller sludge feed rate was required for a seed volume of

40 m3 compared to a seed volume of 80 m

3 or 120 m

3.


Figure 4.35 Run #6 simulation results of pH

Digester 2 TSS

Run # 6 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

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3/2

01

2

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/20

12

7/3

0/2

01

2

7/2

3/2

01

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7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

CO

NC

. (m

gT

SS

/L)

22,000

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Digester 2 pH

Run # 6 Base case

10

/1/2

01

2

9/2

4/2

01

2

9/1

7/2

01

2

9/1

0/2

01

2

9/3

/20

12

8/2

7/2

01

2

8/2

0/2

01

2

8/1

3/2

01

2

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/20

12

7/3

0/2

01

2

7/2

3/2

01

2

7/1

6/2

01

2

7/9

/20

12

7/2

/20

12

6/2

5/2

01

2

6/1

8/2

01

2

6/1

1/2

01

2

6/4

/20

12

5/2

8/2

01

2

5/2

1/2

01

2

5/1

4/2

01

2

5/7

/20

12

4/3

0/2

01

2

4/2

3/2

01

2

4/1

6/2

01

2

pH

7.0

6.0

75

Overall, it can be concluded from Runs #1 to #6 that a seed volume of 120 m3

can

be replaced by a seed volume of 80 m3 in conjunction with the developed sludge feed rate

strategies. But a seed volume of 40 m3 with the developed sludge feed rate strategies and

even adding bicarbonate did not work well. Therefore, a seed volume of 80 m3 with

either F/M or flow proportioned sludge feed rates, even without bicarbonate addition, can

achieve the objectives of a timely, cost-effective, and successful startup.

4.2.3 Optimization of Bicarbonate Concentration

Optimization of digester startup performance by using bicarbonate to maintain pH

and increase alkalinity is implemented in WWTPs. In fact, the amount of bicarbonate

added must be considered in the development of startup strategies. If the pH drops below

6.3, the digester cannot properly cultivate microorganisms (Filbert, 2012). Therefore,

bicarbonate addition is needed. Creating an alkaline environment helps prevent acidic pH

values and maintains normal operation.

Sodium bicarbonate was used by the Regina WWTP to increase pH and

alkalinity. Sodium bicarbonate is highly soluble and will not create a solids deposition

problem since there is no CO2 uptake with its addition; therefore creation of a vacuum is

not possible. The sodium bicarbonate used at the Regina WWTP is 200 lbs/bag (91

kg/bag, 0.25m3).

Based on Run #3 and Run #6, the amount of bicarbonate to be added was

determined to be 1 lb (454 g) sodium bicarbonate per 10 m3 of digester volume.

4.3 Development of Strategies for Optimal Digester Startup

The follow section presents a set of general rules for optimal startup of an

anaerobic digester fed with primary sludge. These rules are based on the advantages of

76

the above simulations, sludge feed rate strategies, proper seed volume, and bicarbonate

concentration.

1. For general WWTPs, the calculation of sludge feed rate during the startup period

is shown in Equation 4.5:

(4.5)

Where, Feed = sludge feed rate each day during digester startup, m3/d.

F/Mreg.= food to mass regression ratio (F/Mregression).

Mass = mass of biological solids synthesized daily, kg/d.

TSPS = Total solids in primary sludge, usually 35 kg/m3.

2. For WWTPs that hauling seed sludge from other plants, the initial amount of seed

sludge is: , m3. (4.6)

3. According to flow proportion feed rate, the sludge feed rate can be increased daily

with the maximum rate increase up to 8% of the previous day’s sludge feed rate

when one of the following conditions occurs: 1) after 45 days of the startup, 2)

when the total solids concentration in the digester reaches about 0.5%, 3) when

the biogas production reaches one third of its normal value. The 8% of feed rate

increase is related to 1/SRTmin. The SRTmin defined in this study is 12 days.

4. For WWTPs transferring seed sludge from adjacent digesters, the minimum



amount of seed sludge is 30% of the digester capacity.

m3 (4.7)

77

5. For WWTPs that cannot afford to transfer or haul large amounts of seed sludge,

the addition of sodium bicarbonate is recommended during the initial stage of the

startup, before pH drops below 6.3. The recommended amount of bicarbonate

addition is:

. (4.8)

6. Gas production can be increased by the addition of scum after the digester has

reached its maximum design feed rate. The scum has a high methane value with a

very low hydraulic rate that provided additional substrate for digestion to promote

greater gas production.

5.0 CONCLUSIONS

This study demonstrated that it is feasible to predict the performance of an

anaerobic digester during startup using an Anaerobic Digestion Model implemented in

BioWinTM

. The BioWinTM

model was calibrated using actual anaerobic digester

performance data from the Regina WWTP for steady state during normal operation and

dynamic state during startup. The BioWinTM

model was used to compute five operating

trends for the following parameters: VFA, pH, alkalinity, digester solids concentration,

and biogas production. These trends provided detailed information about the operation of

the digester during startup and were invaluable in the development of general strategies

for successfully starting up a new or existing anaerobic digester.

The real potential of the BioWinTM

model lies in its capability to provide detailed

information on how to start up a digester in a short amount of time with low cost relative

to alternative experimental approaches such as bench-scale tests. The BioWinTM

model is

advantageous because it allows engineers and plant operators to: (1) identify the best

course of action to start up the digester; (2) reduce the probability of selecting startup

78

conditions that could lead to the development of excessive volatile acids; (3) reduce the

amount of time before consistent biogas production can be achieved; (4) speed up the

evaluation time of digester startup strategies; and (5) tailor startup strategies for unique

sludge characteristics or digester configurations.

The BioWinTM

model was used to provide a better understanding of the effect of

various parameters on the performance of the digester during the startup process. When

attempting to reduce the startup time, three parameters should be carefully considered:

the amount of seed sludge, the primary sludge feed rate, and the addition of pH control

agents. Using high seed sludge amount and low primary sludge feed rates reduces the

likelihood of developing acidic conditions. This is beneficial since the development of

acidic conditions can negatively impact the digester for as long as six to eight weeks.

Using a pH control agent, such as bicarbonate, provides buffering capacity to maintain

the volatile acid to alkalinity ratio to a level suitable for the growth of methanogens. The

addition of bicarbonate reduces the seed sludge volume requirement as well as the need

for a high primary sludge feed rate. However, the bicarbonate dose requires careful

calculation to ensure that sufficient alkalinity is added to the digester.

For WWTPs with limited seed sludge available, either due to poor quality sludge

from adjacent digesters or relatively long hauling distances from another location, plant

operators face a significant challenge in determining a suitable startup strategy that will

allow for a successful startup of the digester in a short period of time while maintaining

the volatile acid to alkalinity ratio to within acceptable levels.

This study has identified cost-effective strategies for the startup of an anaerobic

digester fed with primary sludge. These strategies are summarized as follows:

79

For WWTPs hauling seed sludge from a relatively distant plant, the minimum

amount of seed sludge is 2% of the digester volume (

m3). The primary sludge feed rate should be proportional to the food to

biomass ratio given in Equation 4.5. This combination allows the digester to start

up without exceeding the acceptable level of volatile acids.

The sludge feed rate can be increased daily up to a maximum rate increase of 8%

of the previous day’s sludge feed rate under the following conditions: (1) 45 days

have elapsed since startup; (2) the total solids concentration in the digester has

reached about 0.5%; and (3) the biogas production has reached one third of its

normal value. The 8% feed rate increase is related to 1/SRTmin. The SRTmin

defined in this study was 12 days.

The sludge feed rate cannot exceed the maximum sludge feed rate, otherwise the

methanogenic bacteria may be washed out of the digester. The maximum sludge

feed rate was determined by the volume of the digester divided by the SRTmin.

For WWTPs transferring seed sludge from adjacent digesters, the minimum



amount of seed sludge is 30% of the digester’s capacity.

For WWTPs that cannot afford to transfer or haul large amounts of seed sludge,

the addition of sodium bicarbonate is recommended during the initial stage of

startup (i.e. before a pH drop below 6.3 has been observed). The recommended

amount of bicarbonate addition is 45 g per cube meter of digester volume per day.

80

Early and consistent gas production can be achieved if the above listed guidelines

are followed. Gas production can be increased by the addition of scum after the

digester has reached its maximum design feed rate. This is because scum has a

high methane value with a very low hydraulic rate.

The calibrated and validated BioWinTM

model can be used by any WWTP to

determine the most cost-effective startup strategy that can produce stable digester

operation within a short time span with rapid biogas production. The ability of the

BioWinTM

model to produce accurate predictions of the parameters used for the startup

depends on the correct specification of the wastewater fractions and kinetic parameters.

This research has identified the factors that have significant effects on the predicted gas

production or volatile solids reduction. These factors are: Fxsp, Fup, Fna, the acetoclastic

Mu Max of methanogens, and the acetoclastic decay rate of methanogens. The study also

demonstrated that the accuracy of the BioWinTM

model depends on properly setting the

wastewater characteristics for the following streams: digester filling, seed sludge, scum,

and bicarbonate. Overall, the developed strategies reduced the digester startup time and

costs, while simultaneously reducing the amount of time needed to begin methane gas

production. As such, this model may provide significant economic and environmental

benefits, especially for WWTPs currently facing sludge digestion problems, limited

sludge treatment capacities, and low methane gas production.

It should be emphasized that the above conclusions are limited to anaerobic

digesters fed with primary sludge. The results may be extrapolated to include anaerobic

digesters fed with waste activated sludge or a mixture of primary and waste activated

81

sludge. However, in these cases, careful consideration should be given to the sludge feed

rate.

6.0 RECOMMENDATIONS

Recommendations for further research include the following:

1. To conduct a sensitivity analysis to verify which model parameters used for

calibration most significantly affect model performance.

2. To conduct bench-scale experiments to verify the composition of the wastewater

fractions used in the BioWinTM

model.

3. To conduct a dynamic simulation of an anaerobic digester with scum added to the

feed influent to maximize biogas production.

4. To develop a built-in BioWinTM

Activated Sludge/Anaerobic Digestion Model for

an anaerobic digester fed with either activated sludge alone or a mixture of

primary sludge and activated sludge.

5. To verify the built-in BioWinTM

Activated Sludge/Anaerobic Digestion Model by

pilot testing it at a WWTP where activated sludge is used for digester feed.

82

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88

APPENDIX A

Field Data from the Regina WWTP for Calibration

Table A-1: Raw influent variable for calibration - July, 2007

Day Flow m3/d

Total COD

mgCOD/L

TKN

mgN/L

Total P

mgP/L

Nitrate N

mgN/L pH

Alkalinity

mmol/L

Inorganic

S.S. mgISS/L

Calcium

mg/L

Magnesium

mg/L

DO

mg/L

1-Jul 5.99E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

2-Jul 5.73E+04 5.12E+02 34.6 5.47 7.79 7.82 5 34 79 29 0

3-Jul 6.68E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

4-Jul 6.77E+04 4.56E+02 34.6 5.49 7.79 7.54 5 44 79 29 0

5-Jul 6.80E+04 4.30E+02 34.6 5.37 7.79 7.5 5 34 79 29 0

6-Jul 6.94E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

7-Jul 6.70E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

8-Jul 6.56E+04 3.79E+02 34.6 5.25 7.79 7.58 5 24 79 29 0

9-Jul 6.57E+04 4.86E+02 34.6 5.74 7.79 7.54 5 36 79 29 0

10-Jul 7.02E+04 5.63E+02 37.1 5.54 7.79 7.55 5.12 60 76 32 0

11-Jul 6.64E+04 5.12E+02 34.6 6.02 7.79 7.51 5 38 79 29 0

12-Jul 3.75E+04 3.07E+02 34.6 5.1 7.79 7.54 5 96 79 29 0

13-Jul 3.72E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

14-Jul 6.49E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

15-Jul 6.36E+04 4.71E+02 34.6 5.06 7.79 7.44 5 28 79 29 0

16-Jul 6.57E+04 5.02E+02 34.6 5.66 7.79 7.47 5 36 79 29 0

17-Jul 6.98E+04 4.53E+02 34.7 5.86 7.79 7.46 4.94 76 88 24 0

18-Jul 6.87E+04 4.53E+02 34.6 5.57 7.79 7.47 5 60 79 29 0

19-Jul 6.94E+04 4.40E+02 34.6 6.02 7.79 7.53 5 34 79 29 0

20-Jul 6.85E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

21-Jul 6.70E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

22-Jul 8.64E+04 4.25E+02 34.6 4.74 7.79 7.52 5 18 79 29 0

23-Jul 7.02E+04 3.84E+02 34.6 5.24 7.79 7.5 5 36 79 29 0

24-Jul 6.97E+04 4.30E+02 32 5.14 7.79 7.48 4.94 26 72 32 0

25-Jul 7.03E+04 4.15E+02 34.6 6.13 7.79 7.5 5 76 79 29 0

89

26-Jul 6.93E+04 5.17E+02 34.6 6.03 7.79 7.59 5 60 79 29 0

27-Jul 6.85E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

28-Jul 6.64E+04 4.53E+02 34.6 5.53 7.79 7.53 5 44 79 29 0

29-Jul 6.50E+04 4.30E+02 34.6 5.53 7.79 7.6 5 24 79 29 0

30-Jul 6.58E+04 5.27E+02 34.6 5.53 7.79 7.51 5 44 79 29 0

31-Jul 6.81E+04 4.61E+02 34.6 5.66 7.79 7.47 5 38 79 29 0

Table A-2: Scums 1 & 2 variable for calibration - July, 2007

Day

Scum 1

flow

m3/d

Scum 2

flow

m3/d

Total COD

mgCOD/L

TKN

mgN/L

Total P

mgP/L

Nitrate N

mgN/L pH

Alkalinity

mmol/L

Inorganic S.S.

mgISS/L

Calcium

mg/L

Magnesium

mg/L

DO

mg/L

1-Jul 11.42 11.84 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

2-Jul 8.57 7.67 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

3-Jul 11.60 13.89 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

4-Jul 8.34 7.91 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

5-Jul 17.12 16.50 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

6-Jul 8.57 7.29 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

7-Jul 8.56 11.06 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

8-Jul 8.56 7.82 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

9-Jul 7.03 6.53 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

10-Jul 13.05 13.54 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

11-Jul 9.62 9.31 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

12-Jul 8.34 8.57 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

13-Jul 2.03 2.59 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

14-Jul 5.71 5.49 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

15-Jul 11.46 9.54 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

16-Jul 13.74 13.78 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

17-Jul 17.71 18.25 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

18-Jul 20.01 20.91 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

19-Jul 22.71 20.79 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

20-Jul 22.16 22.85 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

21-Jul 6.99 8.57 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

22-Jul 28.32 28.59 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

23-Jul 3.49 2.85 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

90

24-Jul 9.06E 8.57 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

25-Jul 10.43 11.43 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

26-Jul 5.66 5.71 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

27-Jul 7.46 5.71 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

28-Jul 5.53 5.72 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

29-Jul 9.54 11.36 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

30-Jul 14.33 14.37 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

31-Jul 11.37 11.23 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

91

Table A-3: Sedimentation, splitter, gravity thickener and BFP variable for calibration -

July, 2007

Day Sedimentation

variable m3/d

Splitter ratio

variable m3/d

Gravity thickener underflow

variable m3/d

BFP (dewatering)

underflow m3/d

1-Jul 205 0.8211 80 16.4195

2-Jul 333 0.8153 54 12.3006

3-Jul 377 1.0693 91 18.5818

4-Jul 631 0.8687 78 15.1868

5-Jul 219 1.0352 75 14.1762

6-Jul 358 1.1163 86 16.1968

7-Jul 231 0.9892 0.5 0.0859

8-Jul 260 1.0112 81 15.3267

9-Jul 347 1.0293 76 9.2867

10-Jul 405 1.1492 77 9.8364

11-Jul 447 0.9665 0 0

12-Jul 334 0.9615 0 0

13-Jul 419 0.8936 0 0

14-Jul 151 1.0406 0.7 0.1436

15-Jul 193 0.9962 48 10.2291

16-Jul 232 1.0508 0 0

17-Jul 447 1.0920 80 17.0984

18-Jul 277 0.9718 86 18.4261

19-Jul 274 0.9060 93 18.6919

20-Jul 249 1.0753 0 0

21-Jul 854 1.0353 92 18.5206

22-Jul 173 1.0083 86 17.3108

23-Jul 387 0.9359 88 18.6025

24-Jul 274 1.0642 58 12.2983

25-Jul 430 0.9957 2 0.4449

26-Jul 209 0.9898 69 15.2662

27-Jul 216 0.9623 20 4.3537

28-Jul 226 1.0980 7 14.6016

29-Jul 405 1.0264 83 18.5062

30-Jul 530 0.9882 94 19.7588

31-Jul 192 1.0465 74 15.4899

92

APPENDIX B

Field Data from the Regina WWTP for Validation

Table B-1: Raw influent variable for validation - August, 2007

Day Flow m3/d

Total COD

mgCOD/L

TKN

mgN/L

Total P

mgP/L

Nitrate N

mgN/L pH

Alkalinity

mmol/L

Inorganic S.S.

mgISS/L

Calcium

mg/L

Magnesium

mg/L

DO

mg/L

1-Aug 6.86E+04 461 32.3 5.25 0.01 7.43 4.88 40 92 22 0

2-Aug 6.87E+04 497 32.3 5.64 0.01 7.5 4.88 34 92 22 0

3-Aug 6.73E+04 453 32.3 5.25 0.01 7.49 4.88 21 92 22 0

4-Aug 6.72E+04 453 32.3 5.25 0.01 7.49 4.88 9 92 22 0

5-Aug 6.28E+04 453 32.3 5.25 0.01 7.49 4.88 25 92 22 0

6-Aug 6.10E+04 532 34.6 5.03 0.01 7.31 4.88 74 92 22 0

7-Aug 1.03E+05 507 31.4 5.25 0.01 7.49 4.88 12 92 22 0

8-Aug 7.38E+04 410 32.3 5.25 0.01 7.41 4.88 26 92 22 0

9-Aug 7.26E+04 451 32.3 5.25 0.01 7.47 4.88 32 92 22 0

10-Aug 7.07E+04 453 32.3 5.25 0.01 7.49 4.88 183 92 22 0

11-Aug 8.33E+04 453 32.3 5.25 0.01 7.49 4.88 49 92 22 0

12-Aug 7.15E+04 456 32.3 5.25 0.01 7.5 4.88 28 92 22 0

13-Aug 7.21E+04 292 32.3 5.25 0.01 7.49 4.88 24 92 22 0

14-Aug 6.94E+04 453 32.3 5.25 0.01 7.43 4.88 41 92 22 0

15-Aug 6.75E+04 456 32.3 5.33 0.01 7.43 4.88 90 92 22 0

16-Aug 6.95E+04 492 32.3 5.25 0.01 7.49 4.88 28 92 22 0

17-Aug 6.95E+04 453 32.3 5.25 0.01 7.49 4.88 53 92 22 0

18-Aug 6.93E+04 453 32.3 5.25 0.01 7.49 4.88 87 92 22 0

19-Aug 7.27E+04 466 32.3 5.25 0.01 7.59 4.88 38 92 22 0

20-Aug 7.09E+04 497 32.3 5.29 0.01 7.56 4.88 46 92 22 0

21-Aug 7.33E+04 379 33 4.79 0.01 7.55 4.88 20 92 22 0

22-Aug 7.04E+04 453 32.3 5.35 0.01 7.56 4.88 41 92 22 0

23-Aug 6.86E+04 512 32.3 5.25 0.01 7.58 4.88 76 92 22 0

24-Aug 6.76E+04 453 32.3 5.25 0.01 7.49 4.88 41 92 22 0

25-Aug 6.83E+04 453 32.3 5.25 0.01 7.49 4.88 41 92 22 0

93

26-Aug 6.72E+04 440 32.3 5.25 0.01 7.47 4.88 24 92 22 0

27-Aug 7.09E+04 394 32.3 5.3 0.01 7.56 4.88 30 92 22 0

28-Aug 7.13E+04 430 32.5 5.25 0.01 7.5 4.88 48 92 22 0

29-Aug 7.03E+04 497 32.3 5.25 0.01 7.49 4.88 46 92 22 0

30-Aug 5.95E+04 445 32.3 5.25 0.01 7.49 4.88 34 92 22 0

31-Aug 5.88E+04 453 32.3 5.25 0.01 7.49 4.88 27 92 22 0

Table B-2: Scums 1 & 2 variable for validation - August, 2007

Day Scum1

flow m3/d

Scum2

flow m3/d

Total COD

mgCOD/L

TKN

mgN/L

Total P

mgP/L

Nitrate N

mgN/L pH

Alkalinity

mmol/L

Inorganic S.S.

mgISS/L

Calcium

mg/L

Magnesium

mg/L

DO

mg/L

1-Aug 7.88 5.90 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

2-Aug 16.37 17.13 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

3-Aug 21.43 20.99 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

4-Aug 17.12 18.02 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

5-Aug 17.47 18.09 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

6-Aug 17.68 17.13 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

7-Aug 28.96 28.55 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

8-Aug 15.83 14.75 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

9-Aug 0 0 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

10-Aug 0 0 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

11-Aug 10.73 10.98 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

12-Aug 6.39 8.56 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

13-Aug 8.56 6.08 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

14-Aug 3.18 5.34 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

15-Aug 11.11 9.05 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

16-Aug 5.71 7.56 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

17-Aug 7.72 6.25 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

18-Aug 9.40 9.68 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

19-Aug 9.48 10.32 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

20-Aug 10.51 8.78 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

21-Aug 5.71 7.71 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

22-Aug 8.88 9.21 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

23-Aug 9.26 8.58 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

24-Aug 10.39 10.10 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

94

25-Aug 4.10 4.19 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

26-Aug 5.22 5.71 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

27-Aug 10.66 8.82 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

28-Aug 13.09 14.04 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

29-Aug 9.73 9.72 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

30-Aug 14.28 15.04 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

31-Aug 11.43 11.50 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

95

Table B-3: Sedimentation, splitter, gravity thickener and BFP variable for validation -

August, 2007

Day Sedimentation

variable m3/d

Splitter ratio

variable m3/d

Gravity thickener underflow

variable m3/d

BFP (dewatering)

underflow m3/d

1-Aug 192 1.1223 5.3 1.11177

2-Aug 310 1.0657 86 10.66266

3-Aug 294 1.0493 77 9.54174

4-Aug 288 1.0315 63 7.794

5-Aug 317 0.8778 57 7.04941

6-Aug 354 1.0724 77 9.48649

7-Aug 833 0.99 73 9.06231

8-Aug 187 1.0149 86 10.67081

9-Aug 282 0.9258 83 18.16188

10-Aug 285 0.9389 7.7 16.59871

11-Aug 610 0.9478 48 10.38721

12-Aug 345 0.9913 83 18.01839

13-Aug 258 1.0897 88 20.15393

14-Aug 279 0.8543 72 15.7587

15-Aug 324 0.9357 63 14.05168

16-Aug 546 0.9614 92 17.74352

17-Aug 210 0.8732 86 17.17066

18-Aug 245 1.1547 88 17.57933

19-Aug 396 0.9972 107 21.28226

20-Aug 335 1.0777 108 27.60331

21-Aug 386 0.9098 96 24.01981

22-Aug 350 0.9922 16 3.9528

23-Aug 317 1.0556 9.4 23.49045

24-Aug 418 0.9895 0 0

25-Aug 273 1.1409 0 0

26-Aug 190 0.9607 0 0

27-Aug 183 1.011 69 14.40796

28-Aug 205 1.0318 0.2 0.0371

29-Aug 231 1.0189 1 0.20401

30-Aug 355 1.006 27 5.52479

31-Aug 322 1.0506 73 15.83573

96

APPENDIX C

Steady-state simulation for the Regina WWTP

Figure C-1: Configuration of the Regina WWTP model

Table C-1: Raw influent constant

Name Value

Flow 6.79E+04

Total COD mgCOD/L 455

Total Kjeldahl Nitrogen mgN/L 34.6

Total P mgP/L 5.53

Nitrate N mgN/L 7.79

pH 7.53

Alkalinity mmol/L 5

Inorganic S.S. mgISS/L 55

Calcium mg/L 79

Magnesium mg/L 29

Dissolved oxygen mg/L 0

Table C-2: Lagoon Dimension

Volume m3 Area m

2 Depth m Width m

350000 60344.828 5.8 4

Table C-3: Other units constant

Grit tank flow split underflow constant 260 m3/d

sedimentation tank flow split underflow constant 330 m3/d

Gravity thickener flow split underflow constant 56 m3/d

BFP (dewatering) flow split underflow 9 m3/d

splitter13 flow split constant ratio[S/M] 1.0

Tertiary clarifier flow split underflow 3500 m3/d.

Influent

Digester 1

Grit Tank

Digester 2

Tertiary

Cake

Grit

Lagoon 1 Lagoon 2 Lagoon 3

Sludge

Alum

Scum 1

Scum 2

97

Table C-4: Tertiary effluent

State variable Conc. (mg/L) Mass rate (kg/d) Notes

Non-polyP heterotrophs 0.01 0.47

Anoxic methanol utilizers 0 0

Ammonia oxidizing biomass 0 0.02

Nitrite oxidizing biomass 0 0.01

Anaerobic ammonia oxidizers 0 0

PolyP heterotrophs 0 0

Propionic acetogens 0 0

Acetoclastic methanogens 0 0

Hydrogenotrophic methanogens 0 0

Endogenous products 0.1 6.37

Slowly bio. COD (part.) 0 0.01

Slowly bio. COD (colloid.) 0 0

Part. inert. COD 0.05 3.34

Part. bio. org. N 0 0

Part. bio. org. P 0 0

Part. inert N 0 0.12

Part. inert P 0 0.04

Stored PHA 0 0

Releasable stored polyP 0 0

Fixed stored polyP 0 0

PolyP bound cations 0 0

Readily bio. COD (complex) 0.7 43.83

Acetate 0 0

Propionate 0 0

Methanol 0 0

Dissolved H2 0 0

Dissolved methane 0 0

Ammonia N 0.05 2.86

Sol. bio. org. N 1.02 63.32

Nitrite N 0.01 0.56

Nitrate N 32.33 2010.8

Dissolved nitrogen gas 14.32 890.89

PO4-P (Sol. & Me Complexed) 0.93 57.59

Sol. inert COD 23.96 1490.74

Sol. inert TKN 0.69 43.07

Inorganic S.S. 0.02 1.03

Struvite 0 0

Hydroxy-dicalcium-phosphate 0 0

Hydroxy-apatite 0 0

Magnesium 29.03 1805.99

Calcium 78.96 4911.9

Metal 0 0.26

Other Cations (strong bases) 4.96 308.34 meq/L and keq/d

Other Anions (strong acids) 7.51 467.29 meq/L and keq/d

Total CO2 1.55 96.18 mmol/L and kmol/d

User defined 1 0 0

User defined 2 0 0

User defined 3 0 0

User defined 4 0 0

Dissolved oxygen 2 124.41

98

Table C- 5: Primary effluent to lagoons

State variable Conc. (mg/L) Mass rate (kg/d) Notes

Non-polyP heterotrophs 0.05 3.01

Anoxic methanol utilizers 0.02 1.14

Ammonia oxidizing biomass 0.02 1.13

Nitrite oxidizing biomass 0.02 1.13

Anaerobic ammonia oxidizers 0.02 1.14

PolyP heterotrophs 0.02 1.14

Propionic acetogens 0.02 1.20

Acetoclastic methanogens 0.06 3.66

Hydrogenotrophic methanogens 0.04 2.49

Endogenous products 0.05 3.21

Slowly bio. COD (part.) 94.31 5761.90

Slowly bio. COD (colloid.) 66.58 4067.54

Part. inert. COD 28.57 1745.67

Part. bio. org. N 1.86 113.90

Part. bio. org. P 0.51 30.91

Part. inert N 1.00 60.91

Part. inert P 0.31 19.14

Stored PHA 0.00 0.00

Releasable stored polyP 0.00 0.11

Fixed stored polyP 0.00 0.00

PolyP bound cations 0.00 0.08

Readily bio. COD (complex) 70.83 4327.79

Acetate 12.72 777.27

Propionate 0.01 0.58

Methanol 0 0.00

Dissolved H2 0.00 0.00

Dissolved methane 0.07 4.14

Ammonia N 24.79 1514.86

Sol. bio. org. N 5.25 321.01

Nitrite N 0.00 0.00

Nitrate N 0.50 30.47

Dissolved nitrogen gas 15.96 974.94

PO4-P (Sol. & Me Complexed) 2.53 154.64

Sol. inert COD 26.74 1633.92

Sol. inert TKN 0.76 46.45

Inorganic S.S. 4.63 282.72

Struvite 0 0.00

Hydroxy-dicalcium-phosphate 0 0.00

Hydroxy-apatite 0 0.00

Magnesium 75.89 4636.80

Calcium 0.25 15.55

Metal 0 0.00

Other Cations (strong bases) 5.02 306.66 meq/L and keq/d

Other Anions (strong acids) 8.02 489.75 meq/L and keq/d

Total CO2 5.15 314.72 mmol/L and kmol/d

User defined 1 0 0.00




Dissolved oxygen 0.00 0.00

99

Parameters Value Units

pH 7.29

Ionized ammonium 1.76 mmol/L

Unionized ammonia 0.01 mmol/L

Nitrous acid 0.00 mmol/L

Nitrite 0.00 mmol/L

Total dissolved CO2 0.51 mmol/L

Bicarbonate 4.64 mmol/L

Carbonate 0.01 mmol/L

Unionized ortho-P 0.00 mmol/L

H2PO4- 0.03 mmol/L

HPO4-- 0.05 mmol/L

PO4--- 0.00 mmol/L

Metal phosphate (solid) 0 mmol/L

Metal hydroxide (solid) 0 mmol/L

Metal ion 0 mmol/L

MeH2PO4++ 0 mmol/L

MeHPO4+ 0 mmol/L

Acetic acid 0.00 mmol/L

Acetate 0.20 mmol/L

Propionic acid 0.00 mmol/L

Propionate 0.00 mmol/L

Ionic strength 0.02

Monvalent Act. Coeff. 0.88

Divalent Act. Coeff. 0.60

Trivalent Act. Coeff. 0.32

Flow 61097.03 m3/d

100

APPENDIX D

Field Data from the Regina WWTP and Model Data from Calibration/Validation

for Startup Simulation

Table D- 1: Bicarbonate and seed constant for base case - April to September

Name Bicarbonate constant Seed constant

Flow m3/d 1 8

Non-polyP heterotrophs mgCOD/L 345.55 345.55

Anoxic methanol utilizers mgCOD/L 3.64 3.64

Ammonia oxidizing biomass mgCOD/L 1.98 1.98

Nitrite oxidizing biomass mgCOD/L 1.98 1.98

Anaerobic ammonia oxidizers mgCOD/L 4.47 4.47

PolyP heterotrophs mgCOD/L 3.91 3.91

Propionic acetogens mgCOD/L 15.37 15.37

Acetoclastic methanogens mgCOD/L 400.84 400.84

Hydrogenotrophic methanogens mgCOD/L 244.8 244.8

Endogenous products mgCOD/L 511.16 511.16

Slowly bio. COD (part.) mgCOD/L 6620 6620

Slowly bio. COD (colloid.) mgCOD/L 2.23 2.23

Part. inert. COD mgCOD/L 9581.33 9581.33

Part. bio. org. N mgN/L 162.54 162.54

Part. bio. org. P mgP/L 73.91 73.91

Part. inert N mgN/L 291.16 291.16

Part. inert P mgP/L 91.51 91.51

Stored PHA mgCOD/L 0.69 0.69

Releasable stored polyP mgP/L 0 0

Fixed stored polyP mgP/L 0 0

PolyP bound cations mg/L 0.05 0.05

Readily bio. COD (complex) mgCOD/L 0.58 0.58

Acetate mgCOD/L 126.24 126.24

Propionate mgCOD/L 3.99 3.99

Methanol mgCOD/L 0 0

Dissolved H2 mgCOD/L 0.02 0.02

Dissolved methane mg/L 30.72 30.72

Ammonia N mgN/L 247.99 247.99

Sol. bio. org. N mgN/L 1.65 1.65

Nitrite N mgN/L 0 0

Nitrate N mgN/L 0 0

Dissolved nitrogen gas mgN/L 0.03 0.03

PO4-P (Sol. & Me Complexed) mgP/L 168.19 168.19

Sol. inert COD mgCOD/L 442.39 442.39

Sol. inert TKN mgN/L 0.85 0.85

Inorganic S.S. mgISS/L 2172.16 2172.16

Struvite mgISS/L 0 0

Hydroxy-dicalcium-phosphate mgISS/L 0 0

Hydroxy-apatite mgISS/L 0 0

Magnesium mg/L 29.13 29.13

Calcium mg/L 79.08 79.08

Metal mg/L 0 0

Other Cations (strong bases) meq/L 17.99 17.99

101

Other Anions (strong acids) meq/L 6.16 6.16

Total CO2 mmol/L 38.63 38.63

User defined 1 mg/L 0 0

User defined 2 mg/L 0 0

User defined 3 mgVSS/L 0 0

User defined 4 mgISS/L 0 0

Dissolved oxygen mg/L 0 0

102

Table D-2: Raw influent variable for base case and Run #1 to Run #6 - April to September, 2012

Day Flow m3/d

Total COD

mgCOD/L

TKN

mgN/L

Total P

mgP/L

Nitrate N

mgN/L pH

Alkalinity

mmol/L

Inorganic S.S.

mgISS/L

Calcium

mg/L

Magnesium

mg/L

DO

mg/L

1-Apr 0 660.48 38 5.38 0.225 7.55 6.32 32 84 46 0

2-Apr 0 532.48 38 5.06 0.225 7.52 6.32 28 84 46 0

3-Apr 0 399.36 38 5.64 0.2 7.54 6.32 20 84 46 0

4-Apr 0 501.76 38 4.88 0.225 7.59 6.32 30 84 39 0

5-Apr 5.93E+04 552.96 38 5.53 0.225 7.6 6.32 28 84 39 0

6-Apr 6.39E+04 517.12 38 5.53 0.225 7.6 6.32 34 92 39 0

7-Apr 6.52E+04 778.24 38 5.53 0.225 7.6 6.32 34 92 39 0

8-Apr 8.39E+04 455.68 38 5.53 0.225 7.6 6.22 102 92 39 0

9-Apr 8.14E+04 604.16 38 4.78 0.225 7.46 6.22 18 92 39 0

10-Apr 7.26E+04 798.72 38 11.62 0.225 7.39 6.22 96 92 39 0

11-Apr 7.17E+04 547.84 38 4.77 0.2 7.51 6.22 26 92 39 0

12-Apr 6.91E+04 506.88 38 4.83 0.225 7.65 6.22 22 92 20 0

13-Apr 6.83E+04 542.72 38 5.53 0.225 7.6 6.22 35 92 20 0

14-Apr 6.70E+04 542.72 38 5.53 0.225 7.6 6.86 32 92 20 0

15-Apr 6.61E+04 537.6 38 5.26 0.225 7.64 6.86 22 92 20 0

16-Apr 6.57E+04 537.6 38 4.89 0.225 7.8 6.86 100 92 20 0

17-Apr 6.89E+04 512 38 5.63 0.4 7.81 6.86 44 92 20 0

18-Apr 6.66E+04 501.76 38 5.5 0.225 7.73 6.86 28 88 20 0

19-Apr 6.44E+04 552.96 38 5.26 0.225 7.71 6.86 22 88 20 0

20-Apr 6.16E+04 542.72 38 5.53 0.225 7.6 6.86 9 88 46 0

21-Apr 6.49E+04 542.72 38 5.53 0.225 7.6 7.02 30 88 46 0

22-Apr 6.35E+04 496.64 38 5.78 0.225 7.51 7.02 18 88 46 0

23-Apr 6.48E+04 476.16 38 5.47 0.225 7.65 7.02 28 88 46 0

24-Apr 6.74E+04 599.04 38 5.18 0.1 7.53 7.02 26 88 46 0

25-Apr 6.41E+04 614.4 38 5.36 0.225 7.56 7.02 32 88 46 0

26-Apr 6.28E+04 506.88 38 5.34 0.225 7.66 7.02 50 88 46 0

27-Apr 6.30E+04 542.72 38 5.53 0.225 7.6 6.61 19 88 46 0

28-Apr 6.46E+04 542.72 38 5.53 0.225 7.6 6.61 35 88 46 0

29-Apr 7.07E+04 373.76 38 4.59 0.225 7.6 6.61 12 88 56 0

30-Apr 7.23E+04 496.64 38 5.47 0.225 7.61 6.61 32 88 56 0

1-May 6.86E+04 465.92 38 3.76 0.1 7.48 6.6 26 68 56 0

103

2-May 6.82E+04 481.28 38 4.67 0.2 7.74 6.6 28 68 56 0

3-May 7.39E+04 824.32 38 4.1 0.2 7.73 6.6 44 68 56 0

4-May 6.89E+04 476.16 38 4.1 0.2 7.56 6.6 27 68 56 0

5-May 6.81E+04 476.16 38 4.1 0.2 7.56 6.6 28 68 56 0

6-May 1.05E+05 496.64 38 3.44 0.2 7.56 7.62 36 68 41 0

7-May 8.43E+04 389.12 38 3.64 0.2 7.71 7.62 22 68 41 0

8-May 9.62E+04 512 38 4.02 0.2 7.59 7.62 20 112 41 0

9-May 8.31E+04 614.4 38 4.42 0.2 7.66 7.62 24 112 41 0

10-May 7.57E+04 445.44 38 4.52 0.2 7.63 7.62 40 112 41 0

11-May 7.21E+04 476.16 38 4.1 0.2 7.56 6.72 9 112 41 0

12-May 7.31E+04 476.16 38 4.1 0.2 7.56 6.72 71 112 20 0

13-May 7.15E+04 460.8 38 4.52 0.2 7.64 6.72 26 112 20 0

14-May 6.92E+04 476.16 38 4.1 0.2 7.51 6.72 25 112 20 0

15-May 7.29E+04 373.76 38 4.37 0.2 7.54 6.72 22 112 20 0

16-May 7.30E+04 481.28 38 4.84 0.2 7.51 6.72 18 112 20 0

17-May 7.39E+04 558.08 38 3.94 0.2 7.44 6.72 22 112 20 0

18-May 7.45E+04 476.16 38 4.1 0.2 7.56 5.7 3 140 17 0

19-May 7.33E+04 476.16 38 4.1 0.2 7.56 5.7 91 140 17 0

20-May 7.39E+04 476.16 38 4.1 0.2 7.56 5.7 7 140 17 0

21-May 7.17E+04 450.56 38 4.54 0.2 7.45 5.7 18 140 17 0

22-May 7.00E+04 450.56 38 4.5 0.2 7.45 5.7 48 140 17 0

23-May 7.45E+04 409.6 38 4.06 0.2 7.62 6.68 26 140 27 0

24-May 9.21E+04 430.08 38 4.12 0.2 7.65 6.68 24 140 27 0

25-May 8.33E+04 476.16 38 4.1 0.2 7.56 6.68 21 144 27 0

26-May 8.36E+04 476.16 38 4.1 0.2 7.56 6.68 31 144 27 0

27-May 7.68E+04 394.24 38 3.96 0.2 7.47 6.68 34 144 27 0

28-May 7.03E+04 368.64 38 3.11 0.2 7.49 6.68 18 144 27 0

29-May 1.51E+05 573.44 38 2.89 0.3 7.51 6.68 16 144 27 0

30-May 1.06E+05 373.76 38 4.59 0.2 7.42 6.68 24 144 27 0

31-May 8.77E+04 471.04 38 4.3 0.2 7.43 6.68 20 144 27 0

1-Jun 8.20E+04 465.92 38 4.83 0.67 7.37 5.92 33 108 32 0

2-Jun 7.95E+04 465.92 38 4.83 0.67 7.37 5.92 32 108 32 0

3-Jun 7.37E+04 399.36 38 4.53 0.67 7.37 5.92 12 108 32 0

4-Jun 7.43E+04 322.56 38 4.57 0.67 7.41 5.92 14 108 32 0

5-Jun 7.70E+04 486.4 38 5.66 0.67 7.42 5.92 42 108 32 0

104

6-Jun 7.52E+04 460.8 38 5.68 0.67 7.41 5.92 38 112 29 0

7-Jun 7.43E+04 527.36 38 4.67 0.67 7.36 5.92 26 112 29 0

8-Jun 6.97E+04 465.92 38 4.83 0.67 7.37 5.92 30 112 29 0

9-Jun 6.91E+04 465.92 38 4.83 0.67 7.37 6.2 17 112 29 0

10-Jun 6.85E+04 476.16 38 4.67 0.67 7.34 6.2 24 112 29 0

11-Jun 6.90E+04 373.76 38 4.56 0.67 7.43 6.2 20 112 29 0

12-Jun 7.14E+04 476.16 38 4.64 0.7 7.42 6.2 20 112 29 0

13-Jun 6.97E+04 465.92 38 4.77 0.67 7.36 6.2 20 104 41 0

14-Jun 6.58E+04 286.72 38 3.88 0.67 7.36 6.2 4 104 41 0

15-Jun 6.85E+04 465.92 38 4.83 0.67 7.37 5.8 30 104 41 0

16-Jun 7.02E+04 465.92 38 4.83 0.67 7.37 5.8 18 104 41 0

17-Jun 6.70E+04 517.12 38 4.67 0.67 7.3 5.8 30 104 41 0

18-Jun 6.53E+04 512 38 5.05 0.67 7.45 5.8 26 104 41 0

19-Jun 6.96E+04 501.76 38 5.11 0.67 7.28 5.8 36 104 41 0

20-Jun 6.99E+04 537.6 38 4.82 0.5 7.32 5.8 108 112 41 0

21-Jun 7.39E+04 435.2 38 4.6 0.67 7.39 5.8 34 112 24 0

22-Jun 8.30E+04 465.92 38 4.83 0.67 7.37 5.8 30 112 24 0

23-Jun 7.35E+04 465.92 38 4.83 0.67 7.37 5.8 30 112 24 0

24-Jun 7.03E+04 558.08 38 5.08 0.67 7.32 4.54 30 112 24 0

25-Jun 6.83E+04 496.64 38 5 0.67 7.31 4.54 28 112 24 0

26-Jun 6.87E+04 742.4 38 4.99 0.67 7.34 4.54 120 112 24 0

27-Jun 7.13E+04 552.96 38 5.9 0.67 7.36 4.54 72 112 24 0

28-Jun 8.72E+04 199.68 38 4.34 0.8 7.41 4.54 104 112 24 0

29-Jun 7.50E+04 465.92 38 4.83 0.67 7.37 4.54 30 112 24 0

30-Jun 7.05E+04 465.92 38 4.83 0.37 7.37 4.54 30 112 24 0

1-Jul 7.10E+04 271.36 38 4.11 0.63 7.36 3.3 32 112 29 0

2-Jul 6.41E+04 419.84 38 3.51 0.63 7.29 3.3 50 112 29 0

3-Jul 8.12E+04 419.84 38 3.63 0.63 7.37 3.3 24 112 29 0

4-Jul 8.23E+04 471.04 38 3.97 0.7 7.35 3.3 36 112 29 0

5-Jul 7.49E+04 389.12 38 4.44 0.63 7.32 3.3 20 112 22 0

6-Jul 6.89E+04 399.36 38 4.11 0.63 7.36 3.3 45 136 22 0

7-Jul 6.64E+04 399.36 38 4.11 0.63 7.36 4.6 6 136 22 0

8-Jul 6.67E+04 547.84 38 4.56 0.63 7.26 4.6 34 136 22 0

9-Jul 6.36E+04 583.68 38 4.39 0.63 7.22 4.6 38 136 22 0

10-Jul 6.66E+04 225.28 38 3.55 0.4 7.48 4.6 20 136 22 0

105

11-Jul 6.83E+04 281.6 38 3.85 0.63 7.25 4.6 18 120 29 0

12-Jul 6.76E+04 245.76 38 4.27 0.63 7.44 4.6 10 120 29 0

13-Jul 7.25E+04 399.36 38 4.11 0.63 7.36 4.6 17 120 29 0

14-Jul 6.76E+04 399.36 38 4.11 0.63 7.36 5.18 10 120 29 0

15-Jul 6.65E+04 537.6 38 4.28 0.63 7.32 5.18 22 120 29 0

16-Jul 7.02E+04 322.56 38 3.26 0.63 7.27 5.18 48 120 29 0

17-Jul 7.29E+04 256 38 3.38 0.63 7.41 5.18 24 120 29 0

18-Jul 7.93E+04 348.16 38 3.75 0.63 7.35 5.18 20 120 29 0

19-Jul 9.62E+04 378.88 38 4.13 0.63 7.5 5.02 18 120 20 0

20-Jul 8.06E+04 399.36 38 4.11 0.63 7.36 5.02 24 136 20 0

21-Jul 7.53E+04 399.36 38 4.11 0.63 7.36 5.02 18 136 20 0

22-Jul 7.30E+04 409.6 38 4.44 0.63 7.43 5.02 8 136 20 0

23-Jul 6.95E+04 296.96 38 3.9 0.63 7.3 5.02 14 136 20 0

24-Jul 6.77E+04 465.92 38 4.45 0.63 7.39 5.02 40 136 20 0

25-Jul 7.18E+04 409.6 38 4.5 0.63 7.46 5.02 30 136 20 0

26-Jul 7.65E+04 455.68 38 4.46 0.8 7.43 5.02 20 136 20 0

27-Jul 6.96E+04 399.36 38 4.11 0.63 7.36 4.84 24 108 20 0

28-Jul 6.85E+04 399.36 38 4.11 0.63 7.36 4.84 19 108 20 0

29-Jul 6.70E+04 430.08 38 4.43 0.63 7.38 4.84 10 108 20 0

30-Jul 6.67E+04 496.64 38 4.56 0.63 7.34 4.84 22 108 20 0

31-Jul 6.80E+04 512 38 4.78 0.4 7.37 4.84 20 108 20 0

1-Aug 6.75E+04 327.68 38 4.32 0.65 7.26 4.66 10 80 41 0

2-Aug 6.62E+04 532.48 38 4.83 0.65 7.39 4.66 64 80 41 0

3-Aug 6.85E+04 499.2 38 4.48 0.65 7.29 4.66 25 80 41 0

4-Aug 8.29E+04 499.2 38 4.48 0.65 7.29 4.66 31 80 41 0

5-Aug 7.19E+04 499.2 38 4.48 0.65 7.29 4.66 49 80 41 0

6-Aug 6.59E+04 465.92 38 4.29 0.65 7.2 4.66 32 80 41 0

7-Aug 6.32E+04 486.4 38 4.75 0.65 7.26 4.66 42 80 41 0

8-Aug 6.98E+04 537.6 38 5.25 0.65 7.28 4.66 50 80 41 0

9-Aug 7.07E+04 655.36 38 4.62 0.65 7.24 4.44 30 76 41 0

10-Aug 6.73E+04 499.2 38 4.48 0.65 7.29 4.44 63 76 41 0

11-Aug 6.45E+04 499.2 38 4.48 0.65 7.29 4.44 61 76 41 0

12-Aug 6.36E+04 501.76 38 4.36 0.65 7.29 4.44 38 76 41 0

13-Aug 6.81E+04 527.36 38 4.67 0.65 7.19 4.44 34 76 41 0

14-Aug 6.48E+04 353.28 38 4.1 0.65 7.24 4.44 24 76 41 0

106

15-Aug 6.20E+04 512 38 4.79 0.65 7.25 4.44 35 96 41 0

16-Aug 6.24E+04 537.6 38 4.43 0.65 7.26 4.54 30 96 27 0

17-Aug 6.67E+04 499.2 38 4.48 0.65 7.29 4.54 91 96 27 0

18-Aug 6.56E+04 499.2 38 4.48 0.65 7.29 4.54 55 96 27 0

19-Aug 6.79E+04 527.36 38 4.76 0.65 7.26 4.54 32 96 27 0

20-Aug 6.55E+04 501.76 38 4.62 0.65 7.2 4.54 38 96 27 0

21-Aug 6.74E+04 537.6 38 3.02 0.65 7.19 4.54 54 96 27 0

22-Aug 6.90E+04 501.76 38 4.49 0.65 7.21 4.54 30 96 27 0

23-Aug 6.79E+04 460.8 38 4.42 0.6 7.37 4.54 22 96 27 0

24-Aug 6.53E+04 499.2 38 4.48 0.65 7.29 5.08 17 100 27 0

25-Aug 6.60E+04 499.2 38 4.48 0.65 7.29 5.08 51 100 27 0

26-Aug 6.55E+04 471.04 38 4.6 0.65 7.4 5.08 26 100 27 0

27-Aug 6.56E+04 583.68 38 4.5 0.65 7.47 5.08 30 100 27 0

28-Aug 6.69E+04 532.48 38 4.56 0.7 7.47 5.08 20 100 27 0

29-Aug 6.81E+04 465.92 38 4.42 0.65 7.44 5.08 24 100 27 0

30-Aug 6.70E+04 450.56 38 4.35 0.65 7.2 5.08 2 100 27 0

31-Aug 6.98E+04 499.2 38 4.48 0.65 7.29 5.08 60 100 27 0

1-Sep 6.93E+04 471.04 38 4.32 0.5 7.31 4.78 15 116 27 0

2-Sep 6.84E+04 471.04 38 4.32 0.5 7.31 4.78 20 116 27 0

3-Sep 6.42E+04 537.6 38 4.38 0.5 7.31 4.78 32 116 27 0

4-Sep 6.60E+04 368.64 38 3.64 0.6 7.54 4.78 34 116 27 0

5-Sep 7.04E+04 471.04 38 3.86 0.5 7.45 4.78 32 116 27 0

6-Sep 6.69E+04 512 38 4.6 0.5 7.31 4.78 32 116 76 0

7-Sep 6.62E+04 471.04 38 4.32 0.5 7.31 4.78 38 116 76 0

8-Sep 6.63E+04 471.04 38 4.32 0.5 7.31 4.78 38 116 76 0

9-Sep 6.47E+04 271.36 38 4.62 0.5 7.31 4.78 128 116 76 0

10-Sep 6.52E+04 471.04 38 4.45 0.5 7.39 4.78 24 96 76 0

11-Sep 6.85E+04 337.92 38 4.04 0.3 7.23 4.24 18 96 76 0

12-Sep 6.91E+04 471.04 38 4.26 0.5 7.13 4.24 44 96 15 0

13-Sep 6.78E+04 481.28 38 4.57 0.5 7.1 4.24 52 96 15 0

14-Sep 6.62E+04 471.04 38 4.32 0.5 7.31 4.24 20 96 15 0

15-Sep 6.43E+04 471.04 38 4.32 0.5 7.31 4.24 38 96 15 0

16-Sep 6.50E+04 532.48 38 4.74 0.5 7.3 4.24 36 96 15 0

17-Sep 6.59E+04 517.12 38 4.77 0.5 7.34 4.24 38 96 15 0

18-Sep 6.70E+04 573.44 38 4.6 0.5 7.24 4.24 40 96 15 0

107

19-Sep 6.51E+04 501.76 38 4.61 0.5 7.37 4.24 80 96 15 0

20-Sep 6.43E+04 465.92 38 4.66 0.8 7.38 4.24 54 96 15 0

21-Sep 6.57E+04 471.04 38 4.32 0.5 7.31 4.78 49 0 15 0

22-Sep 6.57E+04 471.04 38 4.32 0.5 7.31 4.78 62 0 76 0

23-Sep 6.68E+04 609.28 38 4.55 0.5 7.22 4.78 46 0 76 0

24-Sep 6.43E+04 496.64 38 4.43 0.3 7.33 4.78 62 0 76 0

25-Sep 6.84E+04 532.48 38 4.67 0.5 7.32 4.78 66 0 76 0

26-Sep 7.03E+04 486.4 38 3.92 0.5 7.32 4.78 46 0 76 0

27-Sep 6.83E+04 266.24 38 2.37 0.5 7.26 4.78 140 0 76 0

28-Sep 6.24E+04 471.04 38 4.32 0.5 7.31 4.78 40 0 76 0

29-Sep 6.32E+04 471.04 38 4.32 0.5 7.31 4.78 44 0 76 0

30-Sep 6.15E+04 522.24 38 4.61 0.5 7.32 4.78 36 0 76 0

108

Table D-3: Sedimentation, splitter13, splitter14, gravity thickener, and BFP dewatering variable for base case and Run #1 to #6 -

April to September, 2012

Day

Sedimentation

underflow

m3/d

Splitter14

rate in side

m3/d

Splitter13

rate in side

m3/d

Gravity

thickener

m3/d

BFP

m3/d

1-Apr 318.5033 0 0 0 0

2-Apr 282.113 0 0 0 0

3-Apr 274.3742 0 0 0 0

4-Apr 390.2159 0 0 0 0

5-Apr 375.8254 0 0 0 0

6-Apr 397.1225 0 0 0 0

7-Apr 628.1489 0 0 0 0

8-Apr 370.9679 0 0 0 0

9-Apr 302.0255 0 0 0 0

10-Apr 347.0012 0 0 0 0

11-Apr 365.971 0 0 0 0

12-Apr 697.0756 0 0 0 0

13-Apr 249.9056 0 0 0 0

14-Apr 351.4481 0 0 0 0

15-Apr 393.0792 0 0 0 0

16-Apr 410.4609 0 0 0 0

17-Apr 743.4112 77.8 55 0 0

18-Apr 201.0419 12.7144 2.7144 0 0

19-Apr 269.8397 3.6058 3.6058 0 0

20-Apr 253.9379 4.6362 4.6362 0 0

21-Apr 328.5813 4.6474 4.6474 0 0

22-Apr 351.988 2.1801 2.1801 0 0

23-Apr 297.8687 1.9089 1.9089 0 0

24-Apr 335.9933 4.7363 4.7363 0.0099 0.0021

25-Apr 257.8903 4.3147 4.3147 4.0097 1.3048

26-Apr 395.0429 8.5108 8.5108 0 0

27-Apr 558.632 7.6077 7.6077 5.1305 1.089

28-Apr 403.5306 15.5827 15.5827 0 0

Day

Sedimentation

underflow

m3/d

Splitter14

rate in side

m3/d

Splitter13

rate in side

m3/d

Gravity

thickene

r m3/d

BFP

m3/d

29-Apr 354.4536 8.4857 8.4857 0 0

30-Apr 292.1409 8.9143 8.9143 1.90E-04 4.04E-05

1-May 296.1627 9.3429 9.3429 0 0

2-May 716.5259 9.8286 9.8286 0 0

3-May 200.3695 0 0 0 0

4-May 262.8477 0 0 0 0

5-May 806.5033 0 0 0 0

6-May 282.3159 0 0 0 0

7-May 532.225 0 0 0 0

8-May 393.2584 0 0 0 0

9-May 322.8099 0 0 0 0

10-May 347.2469 0 0 0 0

11-May 533.4396 0 0 0.0346 0.3213

12-May 210.9001 0 0 0 0

13-May 252.6137 0 0 0 0

14-May 396.8276 0 0 0 0

15-May 356.2964 0 0 0 0

16-May 334.1645 0 0 0 0

17-May 695.4925 1.8815 1.8815 0 0

18-May 330.1543 3.0727 3.0727 0 0

19-May 319.5845 6.1546 6.1546 0 0

20-May 292.4051 6.1914 6.1914 0 0

21-May 347.908 5.5105 5.5105 0 0

22-May 273.4048 6.9055 6.9055 0 0

23-May 575.09 5.053 5.053 0 0

24-May 304.7247 3.2141 3.2141 0 0

25-May 325.0992 8.4199 8.4199 0 0

26-May 313.4002 10.6 10.6 0 0

109

27-May 318.2601 8.322 8.322 0 0

28-May 562.6492 10.4461 10.4461 0 0

29-May 756.9513 8.3862 8.3862 0 0

30-May 394.5494 9.895 9.895 0 0

31-May 319.6203 11.6208 11.6208 0 0

1-Jun 355.2033 5.4556 5.4556 0 0

2-Jun 368.7681 21.0949 21.0949 0 0

3-Jun 398.8006 0 0 0 0

4-Jun 480.1652 16.0385 16.0385 0 0

5-Jun 461.1768 9.1987 9.1987 0 0

6-Jun 543.9571 17.7531 17.7531 0 0

7-Jun 563.7093 12.7242 12.7242 0 0

8-Jun 461.0119 32.8107 32.8107 0 0

9-Jun 516.3666 17.6789 17.6789 0 0

10-Jun 584.8743 16.2113 16.2113 0 0

11-Jun 480.4763 30.1016 30.1016 0 0

12-Jun 589.1598 23.2534 23.2534 0 0

13-Jun 339.2712 43.5277 43.5277 0 0

14-Jun 378.1253 31.0928 31.0928 2.3102 0.5824

15-Jun 321.8088 41.7143 41.7143 0.1577 0.0398

16-Jun 388.4524 75.8219 75.8219 0 0

17-Jun 387.3696 27.9159 27.9159 0 0

18-Jun 422.5364 29.6371 29.6371 2.33E-04 5.87E-05

19-Jun 419.8816 34.7466 34.7466 2.7719 0.6988

20-Jun 433.9396 32.1526 32.1526 0 0

21-Jun 968.3871 31.4817 31.4817 0 0

22-Jun 271.651 46.0477 46.0477 0 0

23-Jun 307.6327 53.9672 53.9672 0 0

24-Jun 228.126 63.3575 63.3575 0 0

25-Jun 392.6119 65.2161 65.2161 0 0

26-Jun 419.9541 38.8503 38.8503 0 0

27-Jun 884.5734 56.4275 56.4275 0 0

28-Jun 371.398 45.0309 45.0309 0 0

29-Jun 173.6706 67.945 67.945 0 0

30-Jun 395.1725 76.8188 76.8188 0 0

1-Jul 255.2906 48.8169 48.8169 0 0

2-Jul 507.795 77.8204 77.8204 9.70E-04 2.07E-04

3-Jul 404.9604 77.8204 77.8204 0 0

4-Jul 208.1162 76.5575 76.5575 0 0

5-Jul 264.164 99.9741 99.9741 8.3418 1.7767

6-Jul 195.4946 73.9083 73.9083 4.2056 0.8958

7-Jul 291.959 79.0681 79.0681 0 0

8-Jul 324.5153 80.0774 80.0774 0 0

9-Jul 335.9541 62.8545 62.8545 0 0

10-Jul 438.7099 126.197 126.197 3.0186 0.6429

11-Jul 655.3126 16.2044 16.2044 1.4582 0.3747

12-Jul 509.3181 116.1675 116.1675 4.9661 1.0577

13-Jul 373.8962 15.2209 15.2209 20.0159 4.2632

14-Jul 288.9957 77.5074 77.5074 0 0

15-Jul 442.9038 106.4443 106.4443 0 0

16-Jul 551.216 70.0909 70.0909 0 0

17-Jul 475.4097 137.0478 137.0478 0 0

18-Jul 848.0601 80.0647 80.0647 0 0

19-Jul 439.3677 95.8452 95.8452 0 0

20-Jul 325.9261 110.1922 110.1922 0 0

21-Jul 349.9327 73.307 73.307 0 0

22-Jul 384.302 107.7786 107.7786 0 0

23-Jul 402.8545 137.0468 137.0468 0 0

24-Jul 451.9229 95.7922 95.7922 21.4113 4.5604

25-Jul 548.3026 155.9171 155.9171 24.0215 3.9178

26-Jul 234.5237 124.5069 124.5069 11.5043 2.4503

27-Jul 230.8126 122.7413 122.7413 4.1401 0.8818

28-Jul 237.6055 123.526 123.526 0 0

29-Jul 460.9095 231.7147 231.7147 0 0

30-Jul 319.7892 58.3726 58.3726 0 0

31-Jul 332.3301 146.8698 146.8698 3.9546 0.8423

1-Aug 411.8029 0 0 0.7011 0.0869

2-Aug 269.0217 145.7148 145.7148 7.526 1.3112

3-Aug 586.8494 175 175 7.1781 1.2506

4-Aug 400.2757 200 200 0 0

110

5-Aug 291.763 157.7418 157.7418 0 0

6-Aug 382.9365 219.1212 219.1212 0 0

7-Aug 337.9727 237.1029 237.1029 0 0

8-Aug 441.1378 251.0108 251.0108 0 0

9-Aug 453.0149 276.2583 276.2583 0 0

10-Aug 456.8218 304.631 304.631 0 0

11-Aug 368.9406 218.7161 218.7161 0 0

12-Aug 460.171 349.8423 349.8423 0 0

13-Aug 400.1712 232.0485 232.0485 0 0

14-Aug 371.9828 242.8192 242.8192 20.7872 3.6212

15-Aug 323.7161 192.0334 192.0334 28.6546 5.2914

16-Aug 357.5099 228.394 228.394 22.9542 3.999

17-Aug 735.7607 470.3267 470.3267 31.0766 5.4141

18-Aug 398.451 212.2712 212.2712 0 0

19-Aug 393.1226 300.0997 300.0997 0 0

20-Aug 449.6321 277.9294 277.9294 6.3457 1.1055

21-Aug 289.5805 136.4959 136.4959 0.1499 0.0278

22-Aug 383.2497 172.4609 172.4609 0.0014 2.49E-04

23-Aug 329.852 235.0477 235.0477 0 0

24-Aug 243.6309 200.1036 200.1036 0 0

25-Aug 499.6831 368.8413 368.8413 0 0

26-Aug 520.666 181.3267 181.3267 0.0094 0

27-Aug 309.9512 229.0754 229.0754 0 0

28-Aug 441.4091 441.3985 441.3985 0 0

29-Aug 587.6841 347.9718 347.9718 0 0

30-Aug 493.5634 382.5317 382.5317 0.2017 0.0351

31-Aug 418.9437 242.4878 242.4878 0 0

1-Sep 253.706 192.4184 192.4184 0 0

2-Sep 274.0539 240.3366 240.3366 5.1561 0.8962

3-Sep 323.7001 271.259 271.259 0 0

4-Sep 515.6249 462.9749 462.9749 0 0

5-Sep 482.484 150.0043 150.0043 28.2777 5.5711

6-Sep 379.0854 378.5656 378.5656 0 0

7-Sep 572.9258 250.0817 250.0817 0 0

8-Sep 455.1451 198.4624 198.4624 0 0

9-Sep 319.8809 290.7265 290.7265 0 0

10-Sep 532.6428 252.5479 252.5479 0.0016 2.73E-04

11-Sep 858.1854 258.0121 258.0121 0 0

12-Sep 251.9159 67 67 1323.679 212.0377

13-Sep 197.4349 197.4349 197.4349 687.6259 119.5186

14-Sep 440.9473 200 200 0 0

15-Sep 396.7048 200 200 0 0

16-Sep 579.1769 200 200 22.4916 4.3566

17-Sep 601.9713 200 200 0 0

18-Sep 325.862 200 200 17.9899 3.1269

19-Sep 203.9442 203.9442 203.9442 0 0

20-Sep 655.5275 200 200 0 0

21-Sep 710.769 100 100 24.9161 4.3308

22-Sep 363.5847 100 100 21.0527 3.6592

23-Sep 332.1617 100 100 7.82E-04 1.36E-04

24-Sep 414.0006 100 100 0.8598 0.1418

25-Sep 302.4747 100 100 30.0629 5.2253

26-Sep 415.0604 100 100 41.1507 7.1525

27-Sep 197.0616 67 67 16.5946 2.8844

28-Sep 223.3473 88 88 7.0544 1.2262

29-Sep 299.2926 100 100 0 0

30-Sep 312.7486 100 100 0 0

111

Table D-4: Scum 2 variable for base case - April to September, 2012

Day Scum 2

flow m3/d

Total COD

mgCOD/L

TKN

mgN/L

Total P

mgP/L

Nitrate N

mgN/L pH

Alkalinity

mmol/L

Inorganic S.S.

mgISS/L

Calcium

mg/L

Magnesium

mg/L

DO

mg/L

1-Apr to

27-Aug 0 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

28-Aug 4.79 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

29-Aug 12.75 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

30-Aug 8.90 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

31-Aug 8.65 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

1-Sep 5.34 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

2-Sep 9.93 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

3-Sep 4.71 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

4-Sep 5.39 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

5-Sep 11.14 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

6-Sep 4.33 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

7-Sep 4.95 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

8-Sep 8.72 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

9-Sep 3.87 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

10-Sep 7.72 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

11-Sep 6.24 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

12-Sep 6.92 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

13-Sep 6.84 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

14-Sep 8.90 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

15-Sep 8.47 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

16-Sep 8.27 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

17-Sep 12.95 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

18-Sep 6.07 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

19-Sep 12.90 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

20-Sep 20.01 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

21-Sep 7.56 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

22-Sep 8.73 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

23-Sep 4.52 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

24-Sep 7.62 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

25-Sep 12.09 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

112

26-Sep 6.77 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

27-Sep 8.61 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

28-Sep 4.25 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

29-Sep 5.64 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

30-Sep 8.41 1.30E+05 130 10 0.2 7.5 5 1 79 29 0

Table D-5: Bicarbonate variable for base case - April to September, 2012

Properties Day with bicarbonate addition Day without

bicarbonate addition

Day 17-May 18-May 28-May 26-Sept Reminder

Flow m3/d 2.5 1.25 0.5 0.25 0

Non-polyP heterotrophs mgCOD/L 354.54 354.54 354.54 354.54 0.01

Anoxic methanol utilizers mgCOD/L 3.63 3.63 3.63 3.63 0.00

Ammonia oxidizing biomass mgCOD/L 1.98 1.98 1.98 1.98 0.00

Nitrite oxidizing biomass mgCOD/L 1.98 1.98 1.98 1.98 0.00

Anaerobic ammonia oxidizers mgCOD/L 4.46 4.46 4.46 4.46 0.00

PolyP heterotrophs mgCOD/L 3.90 3.90 3.90 3.90 0.00

Propionic acetogens mgCOD/L 15.47 15.47 15.47 15.47 0.00

Acetoclastic methanogens mgCOD/L 465.57 465.57 465.57 465.57 0.00

Hydrogenotrophic methanogens mgCOD/L 249.43 249.43 249.43 249.43 0.00

Endogenous products mgCOD/L 517.35 517.35 517.35 517.35 0.10

Slowly bio. COD (part.) mgCOD/L 6,864.20 6,864.20 6,864.20 6,864.20 0.00

Slowly bio. COD (colloid.) mgCOD/L 0.83 0.83 0.83 0.83 0.00

Part. inert. COD mgCOD/L 10,900 10,900 10,900 10,900 0.05

Part. bio. org. N mgN/L 189.57 189.57 189.57 189.57 0.00

Part. bio. org. P mgP/L 72.03 72.03 72.03 72.03 0.00

Part. inert N mgN/L 339.88 339.88 339.88 339.88 0.00

Part. inert P mgP/L 106.82 106.82 106.82 106.82 0.00

113

Stored PHA mgCOD/L 0.69 0.69 0.69 0.69 0.00

Releasable stored polyP mgP/L 0.00 0.00 0.00 0.00 0.00

Fixed stored polyP mgP/L 0.00 0.00 0.00 0.00 0.00

PolyP bound cations mg/L 0.05 0.05 0.05 0.05 0.00

Readily bio. COD (complex) mgCOD/L 0.58 0.58 0.58 0.58 0.70

Acetate mgCOD/L 88.61 88.61 88.61 88.61 0.00

Propionate mgCOD/L 4.14 4.14 4.14 4.14 0.00

Methanol mgCOD/L 0.00 0.00 0.00 0.00 0.00

Dissolved H2 mgCOD/L 0.02 0.02 0.02 0.02 0.00

Dissolved methane mg/L 31.15 31.15 31.15 31.15 0.00

Ammonia N mgN/L 339.57 339.57 339.57 339.57 0.05

Sol. bio. org. N mgN/L 1.88 1.88 1.88 1.88 1.02

Nitrite N mgN/L 0.00 0.00 0.00 0.00 0.01

Nitrate N mgN/L 0.00 0.00 0.00 0.00 32.33

Dissolved nitrogen gas mgN/L 0.03 0.03 0.03 0.03 14.32

PO4-P (Sol. & Me Complexed) mgP/L 158.71 158.71 158.71 158.71 0.93

Sol. inert COD mgCOD/L 412.86 412.86 412.86 412.86 23.96

Sol. inert TKN mgN/L 0.84 0.84 0.84 0.84 0.69

Inorganic S.S. mgISS/L 2,362.29 2,362.29 2,362.29 2,362.29 0.02

Struvite mgISS/L 0.00 0.00 0.00 0.00 0.00

Hydroxy-dicalcium-phosphate mgISS/L 0.00 0.00 0.00 0.00 0.00

Hydroxy-apatite mgISS/L 0.00 0.00 0.00 0.00 0.00

Magnesium mg/L 29.13 29.13 29.13 29.13 29.03

Calcium mg/L 84,500.00 84,500.00 84,500.00 84,500.00 78.96

Metal mg/L 0.00 0.00 0.00 0.00 0.00

Other Cations (strong bases) meq/L 16.94 16.94 16.94 16.94 4.96

Other Anions (strong acids) meq/L 5.95 5.95 5.95 5.95 7.51

114

Total CO2 mmol/L 44.83 44.83 44.83 44.83 1.55

User defined 1 mg/L 0.00 0.00 0.00 0.00 0.00

User defined 2 mg/L 0.00 0.00 0.00 0.00 0.00

User defined 3 mgVSS/L 0.00 0.00 0.00 0.00 0.00

User defined 4 mgISS/L 0.00 0.00 0.00 0.00 0.00

Dissolved oxygen mg/L 0.00 0.00 0.00 0.00 0.00

Table D-6: Seed sludge 40 m3 for base case and Run #3 and #6 - April to September, 2012

Day 1

Apr

2

Apr

3

Apr

4

Apr

5

Apr

6

Apr

7

Apr

8

Apr

9

Apr

10

Apr

11

Apr

12

Apr

13

Apr

14

Apr

15

Apr

16

Apr

14-Apr to

30-Sept

Flow m3/d 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 40 0

Non-polyP heterotrophs

mgCOD/L 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 354.54 0.01

Anoxic methanol utilizers

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.63 0

Ammonia oxidizing biomass

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.98 0

Nitrite oxidizing biomass

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.98 0

Anaerobic ammonia oxidizers

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.46 0

PolyP heterotrophs

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.9 0

Propionic acetogens

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15.47 0

Acetoclastic methanogens

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 465.57 0

Hydrogenotrophic

methanogens mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249.43 0

Endogenous products

mgCOD/L 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 517.35 0.1

115

Slowly bio. COD (part.)

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6864.2 0

Slowly bio. COD (colloid.)

mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.83 0

Part. inert. COD mgCOD/L 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 1.09 0.05

Part. bio. org. N mgN/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 189.57 0

Part. bio. org. P mgP/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72.03 0

Part. inert N mgN/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 339.88 0

Part. inert P mgP/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 106.82 0

Stored PHA mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.69 0

Releasable stored polyP

mgP/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Fixed stored polyP mgP/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

PolyP bound cations mg/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.05 0

Readily bio. COD (complex)

mgCOD/L 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.58 0.7

Acetate mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 88.61 0

Propionate mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.14 0

Methanol mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Dissolved H2 mgCOD/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.02 0

Dissolved methane mg/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31.15 0

Ammonia N mgN/L 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 339.57 0.05

Sol. bio. org. N mgN/L 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.02 1.88 1.02

Nitrite N mgN/L 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0 0.01

Nitrate N mgN/L 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 32.33 0 32.33

Dissolved nitrogen gas

mgN/L 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 14.32 0.03 14.32

PO4-P (Sol. & Me

Complexed) mgP/L 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 158.71 0.93

Sol. inert COD mgCOD/L 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 23.96 412.86 23.96

116

Sol. inert TKN mgN/L 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.84 0.69

Inorganic S.S. mgISS/L 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

2362.2

9 0.02

Struvite mgISS/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Hydroxy-dicalcium-

phosphate mgISS/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Hydroxy-apatite mgISS/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Magnesium mg/L 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.03 29.13 29.03

Calcium mg/L 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 78.96 79.07 78.96

Metal mg/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Other Cations (strong bases)

meq/L 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 4.96 16.94 4.96

Other Anions (strong acids)

meq/L 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 7.51 5.95 7.51

Total CO2 mmol/L 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 44.83 1.55

User defined 1 mg/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

User defined 2 mg/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

User defined 3 mgVSS/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

User defined 4 mgISS/L 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Dissolved oxygen mg/L 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2

117

APPENDIX E

Optimal sludge Feed Rate, Seed and Bicarbonate Addition for Model Run #1 to #6

Table E-1: F/M proportion sludge feed rate for Run #1, #2 and #3 - April to September,

2012

Day

Feed rate

m3/d

Food

kg/d

SRT

d

bCOD in

kg/d

bCOD out

kg/d

Mass

kg/d

Mass

cumulative

kg/d

Mass

regression

unitless

F/M

regression

unitless

Feed rate

calculation

kg/d

17-Apr 10 350 380 262.5 78.8 1.2 181.2 1.1 321 10.9

18-Apr 10 350 380 262.5 78.8 1.2 182.4 1.1 313.1 10.6

19-Apr 10 350 380 262.5 78.8 1.2 183.6 1.1 305.4 10.3

20-Apr 10 350 380 262.5 78.8 1.2 184.7 1.2 297.7 10.1

21-Apr 10 350 380 262.5 78.8 1.2 185.9 1.2 290.2 9.8

22-Apr 11 385 345.5 288.8 86.6 1.4 187.4 1.4 282.7 11.5

23-Apr 11 385 345.5 288.8 86.6 1.4 188.8 1.4 275.4 11.2

24-Apr 11 385 345.5 288.8 86.6 1.4 190.2 1.4 268.2 10.9

25-Apr 11 385 345.5 288.8 86.6 1.4 191.6 1.5 261.1 10.6

26-Apr 11 385 345.5 288.8 86.6 1.4 193.0 1.5 254.1 10.3

27-Apr 12 420 316.7 315.0 94.5 1.7 194.7 1.7 247.2 11.9

28-Apr 12 420 316.7 315.0 94.5 1.7 196.4 1.7 240.4 11.5

29-Apr 12 420 316.7 315.0 94.5 1.7 198.1 1.8 233.8 11.2

30-Apr 12 420 316.7 315.0 94.5 1.7 199.8 1.8 227.2 10.9

1-May 12 420 316.7 315.0 94.5 1.7 201.4 1.9 220.8 10.6

2-May 13 455 292.3 341.3 102.4 2.0 203.4 2.1 214.4 12.0

3-May 14 490 271.4 367.5 110.3 2.3 205.6 2.4 208.2 13.4

4-May 14 490 271.4 367.5 110.3 2.3 207.9 2.4 202.1 13.0

5-May 15 525 253.3 393.8 118.1 2.6 210.5 2.7 196.1 14.4

6-May 16 560 237.5 420.0 126.0 2.9 213.4 2.9 190.2 15.7

7-May 17 595 223.5 446.3 133.9 3.2 216.6 3.2 184.4 17.1

8-May 18 630 211.1 472.5 141.8 3.6 220.2 3.5 178.7 18.4

9-May 19 665 200.0 498.8 149.6 4.0 224.2 3.8 173.1 19.7

10-May 20 700 190.0 525.0 157.5 4.4 228.6 4.2 167.7 21.0

11-May 21 735 181.0 551.3 165.4 4.8 233.4 4.5 162.3 22.3

12-May 22 770 172.7 577.5 173.3 5.2 238.6 4.9 157.1 23.5

13-May 23 805 165.2 603.8 181.1 5.7 244.3 5.3 152.0 24.6

14-May 24 840 158.3 630.0 189.0 6.1 250.4 5.7 146.9 25.8

15-May 25 875 152.0 656.3 196.9 6.6 257.0 6.2 142.0 26.8

16-May 26 910 146.2 682.5 204.8 7.1 264.1 6.6 137.2 27.8

17-May 27 945 140.7 708.8 212.6 7.6 271.7 7.1 132.5 28.8

118

18-May 28 980 135.7 735.0 220.5 8.1 279.9 7.7 128.0 29.7

19-May 29 1,015 131.0 761.3 228.4 8.6 288.5 8.2 123.5 30.5

20-May 30 1,050 126.7 787.5 236.3 9.2 297.7 8.8 119.1 31.3

21-May 31 1,085 122.6 813.8 244.1 9.7 307.4 9.4 114.9 32.0

22-May 32 1,120 118.8 840.0 252.0 10.3 317.7 10.1 110.7 32.6

23-May 33 1,155 115.2 866.3 259.9 10.9 328.6 10.8 106.7 33.2

24-May 35 1,225 108.6 918.8 275.6 12.1 340.7 11.9 102.8 35.5

25-May 37 1,295 102.7 971.3 291.4 13.3 354.0 13.1 99.0 37.7

26-May 39 1,365 97.4 1,023.8 307.1 14.6 368.7 14.3 95.3 39.8

27-May 41 1,435 92.7 1,076.3 322.9 15.9 384.6 15.7 91.7 41.8

28-May 43 1,505 88.4 1,128.8 338.6 17.3 401.9 17.1 88.2 43.6

29-May 45 1,575 84.4 1,181.3 354.4 18.7 420.6 18.6 84.8 45.4

30-May 47 1,645 80.9 1,233.8 370.1 20.2 440.8 20.2 81.6 47.0

31-May 49 1,715 77.6 1,286.3 385.9 21.7 462.5 21.9 78.4 48.5

1-Jun 53 1,855 71.7 1,391.3 417.4 24.7 487.2 24.6 75.4 53.2

2-Jun 55 1,925 69.1 1,443.8 433.1 26.3 513.5 26.6 72.4 54.5

3-Jun 58 2,030 65.5 1,522.5 456.8 28.8 542.2 29.2 69.6 57.2

4-Jun 62 2,170 61.3 1,627.5 488.3 32.1 574.4 32.4 66.9 61.4

5-Jun 66 2,310 57.6 1,732.5 519.8 35.6 609.9 35.9 64.3 65.3

6-Jun 70 2,450 54.3 1,837.5 551.3 39.1 649.1 39.6 61.8 69.1

7-Jun 75 2,625 50.7 1,968.8 590.6 43.8 692.8 44.2 59.4 74.3

8-Jun 80 2,800 47.5 2,100.0 630.0 48.5 741.3 49.0 57.1 79.2

9-Jun 85 2,975 44.7 2,231.3 669.4 53.4 794.7 54.1 55.0 83.8

10-Jun 90 3,150 42.2 2,362.5 708.8 58.4 853.1 59.5 52.9 88.2

11-Jun 97 3,395 39.2 2,546.3 763.9 65.6 918.6 66.6 51.0 95.5

12-Jun 104 3,640 36.5 2,730.0 819.0 72.9 991.5 74.1 49.1 102.4

13-Jun 111 3,885 34.2 2,913.8 874.1 80.5 1072.0 81.9 47.4 109.0

14-Jun 118 4,130 32.2 3,097.5 929.3 88.2 1160.3 90.2 45.8 115.4

15-Jun 125 4,375 30.4 3,281.3 984.4 96.1 1256.4 98.8 44.3 121.6

16-Jun 132 4,620 28.8 3,465.0 1,039.5 104.1 1360.5 107.7 42.9 127.6

17-Jun 139 4,865 27.3 3,648.8 1,094.6 112.3 1472.7 116.9 41.6 133.4

18-Jun 146 5,110 26.0 3,832.5 1,149.8 120.5 1593.3 126.4 40.4 139.2

19-Jun 153 5,355 24.8 4,016.3 1,204.9 128.9 1722.1 136.0 39.4 144.9

20-Jun 160 5,600 23.8 4,200.0 1,260.0 137.3 1859.5 145.8 38.4 150.7

21-Jun 167 5,845 22.8 4,383.8 1,315.1 145.9 2,005.4 155.6 37.6 156.6

22-Jun 174 6,090 21.8 4,567.5 1,370.3 154.5 2,159.9 165.4 36.8 162.6

23-Jun 181 6,335 21.0 4,751.3 1,425.4 163.2 2,323.2 175.0 36.2 168.8

24-Jun 188 6,580 20.2 4,935.0 1,480.5 172.0 2,495.2 184.4 35.7 175.4

25-Jun 195 6,825 19.5 5,118.8 1,535.6 180.9 2,676.1 193.5 35.3 182.3

26-Jun 202 7,070 18.8 5,302.5 1,590.8 189.8 2,865.9 202.2 35.0 189.6

27-Jun 209 7,315 18.2 5,486.3 1,645.9 198.8 3,064.7 210.3 34.8 197.5

119

28-Jun 216 7,560 17.6 5,670.0 1,701.0 207.8 3,272.5 217.9 34.7 206.1

29-Jun 223 7,805 17.0 5,853.8 1,756.1 216.9 3,489.5 224.7 34.7 215.3

30-Jun 230 8,050 16.5 6,037.5 1,811.3 226.1 3,715.5 230.8 34.9 225.2

1-Jul 237 8,295 16.0 6,221.3 1,866.4 235.2 3,950.8 236.2 35.1 236.1

2-Jul 244 8,540 15.6 6,405.0 1,921.5 244.5 4,195.2 240.7 35.5 247.8

3-Jul 251 8,785 15.1 6,588.8 1,976.6 253.7 4,449.0 244.3 36.0 260.6

4-Jul 258 9,030 14.7 6,772.5 2,031.8 263 4,712.0 247.2 36.5 274.6

1-Apr to 16-Apr sludge feed rate 0 m3/d

5-Jul to 30-Sept sludge feed rate 275 m3/d

Table E-2: Flow proportion sludge feed rate for Run #4, #5 and #6 - April to September,

2012

Day Feed rate m3/d Day Feed rate m

3/d Day m

3/d Feed rate m

3

1-Apr 0 2-May 12 2-Jun 54

2-Apr 0 3-May 13 3-Jun 57

3-Apr 0 4-May 13 4-Jun 61

4-Apr 0 5-May 14 5-Jun 65

5-Apr 0 6-May 16 6-Jun 69

6-Apr 0 7-May 17 7-Jun 74

7-Apr 0 8-May 18 8-Jun 79

8-Apr 0 9-May 20 9-Jun 84

9-Apr 0 10-May 21 10-Jun 88

10-Apr 0 11-May 22 11-Jun 95

11-Apr 0 12-May 23 12-Jun 102

12-Apr 0 13-May 25 13-Jun 109

13-Apr 0 14-May 26 14-Jun 115

14-Apr 0 15-May 27 15-Jun 122

15-Apr 0 16-May 28 16-Jun 128

16-Apr 0 17-May 29 17-Jun 133

17-Apr 10 18-May 30 18-Jun 139

18-Apr 10 19-May 31 19-Jun 145

19-Apr 10 20-May 31 20-Jun 151

20-Apr 10 21-May 32 21-Jun 157

21-Apr 10 22-May 33 22-Jun 163

22-Apr 11 23-May 33 23-Jun 169

23-Apr 11 24-May 35 24-Jun 175

24-Apr 11 25-May 38 25-Jun 182

25-Apr 11 26-May 40 26-Jun 190

26-Apr 11 27-May 42 27-Jun 198

27-Apr 12 28-May 44 28-Jun 206

28-Apr 12 29-May 45 29-Jun 215

29-Apr 12 30-May 47 30-Jun 225

30-Apr 12 31-May 49 1-Jul 236

1-May 12 1-Jun 53 2-Jul 248

3-Jul 266

4-Jul to 30-Sept 266 m3/d

120

Table E-3: Seed sludge for 80 m3 Run #2 and #5 - April to September, 2012

Day 1-Apr to 15-Apr 16-Apr 17-Apr 18-Apr to30-Sept

Flow m3/d 800 40 40 0

Non-polyP heterotrophs mgCOD/L 0.01 354.54 354.54 0.01

Anoxic methanol utilizers mgCOD/L 0 3.63 3.63 0

Ammonia oxidizing biomass mgCOD/L 0 1.98 1.98 0

Nitrite oxidizing biomass mgCOD/L 0 1.98 1.98 0

Anaerobic ammonia oxidizers mgCOD/L 0 4.46 4.46 0

PolyP heterotrophs mgCOD/L 0 3.9 3.9 0

Propionic acetogens mgCOD/L 0 15.47 15.47 0

Acetoclastic methanogens mgCOD/L 0 465.57 465.57 0

Hydrogenotrophic methanogens mgCOD/L 0 249.43 249.43 0

Endogenous products mgCOD/L 0.1 517.35 517.35 0.1

Slowly bio. COD (part.) mgCOD/L 0 6864.2 6864.2 0

Slowly bio. COD (colloid.) mgCOD/L 0 0.83 0.83 0

Part. inert. COD mgCOD/L 0.05 1.09 1.09 0.05

Part. bio. org. N mgN/L 0 189.57 189.57 0

Part. bio. org. P mgP/L 0 72.03 72.03 0

Part. inert N mgN/L 0 339.88 339.88 0

Part. inert P mgP/L 0 106.82 106.82 0

Stored PHA mgCOD/L 0 0.69 0.69 0

Releasable stored polyP mgP/L 0 0 0 0

Fixed stored polyP mgP/L 0 0 0 0

PolyP bound cations mg/L 0 0.05 0.05 0

Readily bio. COD (complex) mgCOD/L 0.7 0.58 0.58 0.7

Acetate mgCOD/L 0 88.61 88.61 0

121

Propionate mgCOD/L 0 4.14 4.14 0

Methanol mgCOD/L 0 0 0 0

Dissolved H2 mgCOD/L 0 0.02 0.02 0

Dissolved methane mg/L 0 31.15 31.15 0

Ammonia N mgN/L 0.05 339.57 339.57 0.05

Sol. bio. org. N mgN/L 1.02 1.88 1.88 1.02

Nitrite N mgN/L 0.01 0 0 0.01

Nitrate N mgN/L 32.33 0 0 32.33

Dissolved nitrogen gas mgN/L 14.32 0.03 0.03 14.32

PO4-P (Sol. & Me Complexed) mgP/L 0.93 158.71 158.71 0.93

Sol. inert COD mgCOD/L 23.96 412.86 412.86 23.96

Sol. inert TKN mgN/L 0.69 0.84 0.84 0.69

Inorganic S.S. mgISS/L 0.02 2,362 2,362 0.02

Struvite mgISS/L 0 0 0 0

Hydroxy-dicalcium-phosphate mgISS/L 0 0 0 0

Hydroxy-apatite mgISS/L 0 0 0 0

Magnesium mg/L 29.03 29.13 29.13 29.03

Calcium mg/L 78.96 79.07 79.07 78.96

Metal mg/L 0 0 0 0

Other Cations (strong bases) meq/L 4.96 16.94 16.94 4.96

Other Anions (strong acids) meq/L 7.51 5.95 5.95 7.51

Total CO2 mmol/L 1.55 44.83 44.83 1.55

User defined 1 mg/L 0 0 0 0

User defined 2 mg/L 0 0 0 0

User defined 3 mgVSS/L 0 0 0 0

User defined 4 mgISS/L 0 0 0 0

Dissolved oxygen mg/L 2 0 0 2

122

Table E-4: Seed sludge 120 m3 for Run #1 and #4 - April to September, 2012

Day 1-Apr to 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr to 30-Sept

Flow m3/d 800 40 40 40 0

Non-polyP heterotrophs mgCOD/L 0.01 354.54 354.54 354.54 0.01

Anoxic methanol utilizers mgCOD/L 0 3.63 3.63 3.63 0

Ammonia oxidizing biomass mgCOD/L 0 1.98 1.98 1.98 0

Nitrite oxidizing biomass mgCOD/L 0 1.98 1.98 1.98 0

Anaerobic ammonia oxidizers mgCOD/L 0 4.46 4.46 4.46 0

PolyP heterotrophs mgCOD/L 0 3.9 3.9 3.9 0

Propionic acetogens mgCOD/L 0 15.47 15.47 15.47 0

Acetoclastic methanogens mgCOD/L 0 465.57 465.57 465.57 0

Hydrogenotrophic methanogens mgCOD/L 0 249.43 249.43 249.43 0

Endogenous products mgCOD/L 0.1 517.35 517.35 517.35 0.1

Slowly bio. COD (part.) mgCOD/L 0 6,864.2 6,864.2 6,864.2 0

Slowly bio. COD (colloid.) mgCOD/L 0 0.83 0.83 0.83 0

Part. inert. COD mgCOD/L 0.05 1.09 1.09 1.09 0.05

Part. bio. org. N mgN/L 0 189.57 189.57 189.57 0

Part. bio. org. P mgP/L 0 72.03 72.03 72.03 0

Part. inert N mgN/L 0 339.88 339.88 339.88 0

Part. inert P mgP/L 0 106.82 106.82 106.82 0

Stored PHA mgCOD/L 0 0.69 0.69 0.69 0

Releasable stored polyP mgP/L 0 0 0 0 0

Fixed stored polyP mgP/L 0 0 0 0 0

PolyP bound cations mg/L 0 0.05 0.05 0.05 0

Readily bio. COD (complex) mgCOD/L 0.7 0.58 0.58 0.58 0.7

Acetate mgCOD/L 0 88.61 88.61 88.61 0

123

Propionate mgCOD/L 0 4.14 4.14 4.14 0

Methanol mgCOD/L 0 0 0 0 0

Dissolved H2 mgCOD/L 0 0.02 0.02 0.02 0

Dissolved methane mg/L 0 31.15 31.15 31.15 0

Ammonia N mgN/L 0.05 339.57 339.57 339.57 0.05

Sol. bio. org. N mgN/L 1.02 1.88 1.88 1.88 1.02

Nitrite N mgN/L 0.01 0 0 0 0.01

Nitrate N mgN/L 32.33 0 0 0 32.33

Dissolved nitrogen gas mgN/L 14.32 0.03 0.03 0.03 14.32

PO4-P (Sol. & Me Complexed) mgP/L 0.93 158.71 158.71 158.71 0.93

Sol. inert COD mgCOD/L 23.96 412.86 412.86 412.86 23.96

Sol. inert TKN mgN/L 0.69 0.84 0.84 0.84 0.69

Inorganic S.S. mgISS/L 0.02 2,362.29 2,362.29 2,362.29 0.02

Struvite mgISS/L 0 0 0 0 0

Hydroxy-dicalcium-phosphate mgISS/L 0 0 0 0 0

Hydroxy-apatite mgISS/L 0 0 0 0 0

Magnesium mg/L 29.03 29.13 29.13 29.13 29.03

Calcium mg/L 78.96 79.07 79.07 79.07 78.96

Metal mg/L 0 0 0 0 0

Other Cations (strong bases) meq/L 4.96 16.94 16.94 16.94 4.96

Other Anions (strong acids) meq/L 7.51 5.95 5.95 5.95 7.51

Total CO2 mmol/L 1.55 44.83 44.83 44.83 1.55

User defined 1 mg/L 0 0 0 0 0

User defined 2 mg/L 0 0 0 0 0

User defined 3 mgVSS/L 0 0 0 0 0

User defined 4 mgISS/L 0 0 0 0 0

Dissolved oxygen mg/L 2 0 0 0 2

124

Table E-5: Bicarbonate addition for Run #3 and #6 - April to September, 2012

Run #3 Run #6

Day 16-Apr

25-Apr to

14-May Remainder 16-Apr

28-Apr to

17-May Remainder

Flow m3/d 0.50 0.50 0/d 0.50 0.50 0

Non-polyP heterotrophs

mgCOD/L 354.54 354.54 0.01 354.54 354.54 0.01

Anoxic methanol utilizers

mgCOD/L 3.63 3.63 0.00 3.63 3.63 0.00

Ammonia oxidizing biomass

mgCOD/L 1.98 1.98 0.00 1.98 1.98 0.00

Nitrite oxidizing biomass

mgCOD/L 1.98 1.98 0.00 1.98 1.98 0.00

Anaerobic ammonia oxidizers

mgCOD/L 4.46 4.46 0.00 4.46 4.46 0.00

PolyP heterotrophs mgCOD/L 3.90 3.90 0.00 3.90 3.90 0.00

Propionic acetogens

mgCOD/L 15.47 15.47 0.00 15.47 15.47 0.00

Acetoclastic methanogens

mgCOD/L 465.57 465.57 0.00 465.57 465.57 0.00

Hydrogenotrophic

methanogens mgCOD/L 249.43 249.43 0.00 249.43 249.43 0.00

Endogenous products

mgCOD/L 517.35 517.35 0.10 517.35 517.35 0.10

Slowly bio. COD (part.)

mgCOD/L 6,864.2 6,864.2 0.00 6,864.2 6,864.2 0.00

Slowly bio. COD (colloid.)

mgCOD/L 0.83 0.83 0.00 0.83 0.83 0.00

Part. inert. COD mgCOD/L 10,900 10,900 0.05 10,900 10,900 0.05

Part. bio. org. N mgN/L 189.57 189.57 0.00 189.57 189.57 0.00

Part. bio. org. P mgP/L 72.03 72.03 0.00 72.03 72.03 0.00

Part. inert N mgN/L 339.88 339.88 0.00 339.88 339.88 0.00

Part. inert P mgP/L 106.82 106.82 0.00 106.82 106.82 0.00

Stored PHA mgCOD/L 0.69 0.69 0.00 0.69 0.69 0.00

Releasable stored polyP

mgP/L 0.00 0.00 0.00 0.00 0.00 0.00

Fixed stored polyP mgP/L 0.00 0.00 0.00 0.00 0.00 0.00

PolyP bound cations mg/L 0.05 0.05 0.00 0.05 0.05 0.00

Readily bio. COD (complex)

mgCOD/L 0.58 0.58 0.70 0.58 0.58 0.70

Acetate mgCOD/L 88.61 88.61 0.00 88.61 88.61 0.00

Propionate mgCOD/L 4.14 4.14 0.00 4.14 4.14 0.00

Methanol mgCOD/L 0.00 0.00 0.00 0.00 0.00 0.00

Dissolved H2 mgCOD/L 0.02 0.02 0.00 0.02 0.02 0.00

Dissolved methane mg/L 31.15 31.15 0.00 31.15 31.15 0.00

Ammonia N mgN/L 339.57 339.57 0.05 339.57 339.57 0.05

Sol. bio. org. N mgN/L 1.88 1.88 1.02 1.88 1.88 1.02

125

Nitrite N mgN/L 0.00 0.00 0.01 0.00 0.00 0.01

Nitrate N mgN/L 0.00 0.00 32.33 0.00 0.00 32.33

Dissolved nitrogen gas mgN/L 0.03 0.03 14.32 0.03 0.03 14.32

PO4-P (Sol. & Me

Complexed) mgP/L 158.71 158.71 0.93 158.71 158.71 0.93

Sol. inert COD mgCOD/L 412.86 412.86 23.96 412.86 412.86 23.96

Sol. inert TKN mgN/L 0.84 0.84 0.69 0.84 0.84 0.69

Inorganic S.S. mgISS/L 2,362.29 2,362.29 0.02 2,362.29 2,362.29 0.02

Struvite mgISS/L 0.00 0.00 0.00 0.00 0.00 0.00

Hydroxy-dicalcium-phosphate

mgISS/L 0.00 0.00 0.00 0.00 0.00 0.00

Hydroxy-apatite mgISS/L 0.00 0.00 0.00 0.00 0.00 0.00

Magnesium mg/L 29.13 29.13 29.03 29.13 29.13 29.03

Calcium mg/L 84,500 84,500 78.96 84,500 84,500 78.96

Metal mg/L 0.00 0.00 0.00 0.00 0.00 0.00

Other Cations (strong bases)

meq/L 16.94 16.94 4.96 16.94 16.94 4.96

Other Anions (strong acids)

meq/L 5.95 5.95 7.51 5.95 5.95 7.51

Total CO2 mmol/L 44.83 44.83 1.55 44.83 44.83 1.55

User defined 1 mg/L 0.00 0.00 0.00 0.00 0.00 0.00

User defined 2 mg/L 0.00 0.00 0.00 0.00 0.00 0.00

User defined 3 mgVSS/L 0.00 0.00 0.00 0.00 0.00 0.00

User defined 4 mgISS/L 0.00 0.00 0.00 0.00 0.00 0.00

Dissolved oxygen mg/L 0.00 0.00 0.00 0.00 0.00 0.00

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