Ozonation of pharmaceutical residues in a wastewater treatment …liu.diva-portal.org/smash/get/diva2:1250703/FULLTEXT01.pdf · 2018-09-24 · By implementing an ozonation step (treatment

Linköpings universitetSE–581 83 Linköping

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Linköping University | Department of Physics, Chemistry and BiologyMaster thesis, 30 ECTS | Technical Biology

2018 | LITH-IFM-A-EX--18/3490--SE

Ozonation of pharmaceuticalresidues in a wastewatertreatment plant– Modeling the ozone demand based on a multivariateanalysis of influential parameters

Ozonering av läkemedelsrester på ett avloppsreningsverk-Modellering baserat på en multivariat analys av parametrarsom påverkar ozonbehovet

Emilia JohanssonErica Engberg

Supervisor : Robert GustavssonExaminer : Carl-Fredrik Mandenius

External supervisor : Robert Sehlén

http://www.liu.se

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2018-06-01

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Titel

Ozonation of pharmaceutical residues in a wastewater - Modeling the ozone demand based on multivariate analysis of influential parameters

Författare

Emilia Johansson Erica Engberg

Nyckelord Ozone, Ozonation, Wastewater treatment plant, Wastewater, Pharmaceutical residues, Modeling

Sammanfattning Most pharmaceutical residues in wastewater treatment plants (WWTPs) end up in the hydrosphere where they cause negative

effects on the aquatic life and might disrupt ecosystems. By implementing an ozonation step (treatment with ozone) in the wastewater treatment process, these pharmaceutical residues can be reduced. The purpose of this project was to verify that the

ozonation process works in full-scale, thereby verifying a pilot study conducted in 2014 at Tekniska Verken i Linköping AB

(TVAB). Additionally, the purpose was to investigate which parameters influence the ozone demand in order to formulate a model for the ozone demand. The initial phases during this thesis were a pre-study and a literature study. This was followed by

the multivariate analysis and model construction based on different data from the pilot study. Measurements were performed on

the wastewater in the full-scale facility in order to verify the results from the pilot study. Moreover, measurements were performed to find new ozone consuming parameters. The reduction of pharmaceutical residues was similar to the pilot study,

although slightly lower. Several parameters and factors that were different between pilot study and new measurements affected

the reduction of pharmaceutical residues. For example, DOC and nitrate concentrations have increased since the pilot study in 2014. Also, factors such as the growth in population in Linköping and the differences in design between the pilot plant and the

full-scale facility have influenced the reduction of pharmaceutical residues. A control strategy based on a linear relationship between ozone sensitive Ultra Violet Absorption (UVA) left and remaining pharmaceutical residues after ozonation could

potentially be used. Moreover, three models were constructed and the Multivariate Analysis 1 (MVA1)-model was deemed as

the best, this model includes ozone residual, nitrite, turbidity, simulated Chemical Oxygen Demand (COD(sim)) and ozone dose. The variations in the dose compared to the input parameters for the validation data show that the model predict the ozone

dose well. However, in future other interesting parameters can be included in the model to further improve the accuracy in the ozone dose predicted by the model.

Upphovsrätt

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Copyright

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c© Emilia JohanssonErica Engberg

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Abstract

Most pharmaceutical residues in wastewater treatment plants (WWTPs) end up in the hy-drosphere where they cause negative effects on the aquatic life and might disrupt ecosys-tems. By implementing an ozonation step (treatment with ozone) in the wastewater treat-ment process, these pharmaceutical residues can be reduced. The purpose of this projectwas to verify that the ozonation process works in full-scale, thereby verifying a pilot studyconducted in 2014 at Tekniska Verken i Linköping AB (TVAB). Additionally, the purposewas to investigate which parameters influence the ozone demand in order to formulate amodel for the ozone demand. The initial phases during this thesis were a pre-study anda literature study. This was followed by the multivariate analysis and model construc-tion based on different data from the pilot study. Measurements were performed on thewastewater in the full-scale facility in order to verify the results from the pilot study. More-over, measurements were performed to find new ozone consuming parameters. The reduc-tion of pharmaceutical residues was similar to the pilot study, although slightly lower. Sev-eral parameters and factors that were different between pilot study and new measurementsaffected the reduction of pharmaceutical residues. For example, DOC and nitrate concen-trations have increased since the pilot study in 2014. Also, factors such as the growth inpopulation in Linköping and the differences in design between the pilot plant and the full-scale facility have influenced the reduction of pharmaceutical residues. A control strategybased on a linear relationship between ozone sensitive Ultra Violet Absorption (UVA) leftand remaining pharmaceutical residues after ozonation could potentially be used. More-over, three models were constructed and the Multivariate Analysis 1 (MVA1)-model wasdeemed as the best, this model includes ozone residual, nitrite, turbidity, simulated Chem-ical Oxygen Demand (COD(sim)) and ozone dose. The variations in the dose compared tothe input parameters for the validation data show that the model predict the ozone dosewell. However, in future other interesting parameters can be included in the model tofurther improve the accuracy in the ozone dose predicted by the model.

Acknowledgments

We would like to thank TVAB and the department of Vatten och Avlopp for providing theopportunity of this thesis on a new and exiting matter. Secondly, we express our deepestgratitude to Robert Sehlén, our supervisor at TVAB, for valuable input, advice, guidanceand never fading interest even when things did not go as planned. Thirdly, we thank ourLiU supervisor Robert Gustavsson for his willingness to help and quick response no matterwhat the question might have been. Fourthly, we thank our examiner from LiU, Carl-FredrikMandenius for allocating some of his precious time to this thesis and for his input. Fiftly, weacknowledge the laboratory at TVAB, the laboratory at Aarhus university and SYNLAB forrunning measurements for us. Also, we thank our opponents for their suggestions on howto improve this report. Lastly, we express our gratitude to Maja Ekblad, Ulf Miehe, MichaelStapf and Alexander Sanner for valuable discussions, input and support during this thesis.

v

Abbreviations

COD - Chemical Oxygen DemandCOD(sim)- Simulated Chemical Oxygen DemandCWPharma - Clear Waters from PharmaceuticalsDOC - Dissolved Organic CarbonFe2+ - Bivalent IronFNU- Formazin Nephelometric UnitMBBR- Moving Bed Biofilm ReactormS/m- mikroSiemens/meterMTE- Mass Transfer EfficiencyMVA- Multivariate AnalysisN-model- Nitrogen ModelNO2-N- Nitrite NitrogenPCA - Principal Component AnalysisPLS - Partial Least SquaresTVAB - Tekniska Verken ABUVA- Ultra-Violet AbsorptionWWTP- Wastewater Treatment PlantMS-sim- MATLAB Simulink- simulation

vi

Contents

Abstract iv

Acknowledgments v

Abbreviations vi

Contents vii

List of Figures ix

List of Tables x

1 Introduction 11.1 Purpose of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Objectives of the work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Expected impact of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Theory and Methodology 42.1 Scientific background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.1 Ozone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Generating ozone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Reaction mechanism of ozone . . . . . . . . . . . . . . . . . . . . . . . . . 5Parameters that influence the ozonation of pharmaceutical residues . . . 6

2.1.2 Pharmaceuticals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Pharmaceuticals to be monitored . . . . . . . . . . . . . . . . . . . . . . . 8Effects of pharmaceuticals . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1.3 Wastewater treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Nykvarnsverket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Pharmaceutical residue treatment . . . . . . . . . . . . . . . . . . . . . . 12

2.1.4 Pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Possible Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.1 Pre-study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.2 Literature study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.3 Main activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Multivariate analysis and regression . . . . . . . . . . . . . . . . . . . . . 18Measurements on full-scale facility . . . . . . . . . . . . . . . . . . . . . . 18

2.2.4 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.2.5 Verification and testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

vii

Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.1 PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.2 PLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3 Method and Materials 213.1 Method and materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.1.1 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.1.2 Multivariate analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23PLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Regression - fitted line plot . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1.3 Model validation and testing . . . . . . . . . . . . . . . . . . . . . . . . . . 23Model validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Model testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.1.4 Laboratory work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Sampling days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Ozonation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1.5 Comparison between the pilot study and new measurements . . . . . . . . 26

4 Results and Discussion 274.1 Verification of the pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.1.1 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.1.2 Gas flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.1.3 Pharmaceutical residues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Model construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.2.1 Multivariate analysis and regression model . . . . . . . . . . . . . . . . . . 38

MVA-models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Nitrite model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2.2 Model selection and validation . . . . . . . . . . . . . . . . . . . . . . . . . 414.2.3 Model testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

The procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Ozone Dose(sim) validation . . . . . . . . . . . . . . . . . . . . . . . . . . 45Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.3 Additional aspects to consider . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.3.1 New parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.3.2 Model improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5 Conclusion 505.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Bibliography 53

A Appendix A 58A.1 MATLAB script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

B Appendix B 59B.1 Purpose, objectives and boundary conditions . . . . . . . . . . . . . . . . . . . . 59B.2 Time plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

viii

List of Figures

2.1 The structure of A) Diclofenac, B) Metoprolol and C) Oxazepam . . . . . . . . . . . 92.2 The wastewater treatment process at Nykvarnsverket. . . . . . . . . . . . . . . . . . 122.3 The ozonation process with ozone generation from liquid oxygen and on-line mea-

surement points (On 1, On 2 and On 3). . . . . . . . . . . . . . . . . . . . . . . . . . 132.4 A timeline illustrating how the work was carried out. . . . . . . . . . . . . . . . . . 17

4.1 Daily variations of COD(sim) extracted from Linköpingsmodellen and measuredvariations in DOC, UVA, nitrite and flow during the pilot study. . . . . . . . . . . . 28

4.2 The ozone sensitive UVA left against A) The ozone dose, B) The ozone dose di-vided by DOC concentration C) The ozone dose divided by nitrite concentrations. 31

4.3 The ozone residual against the ozone dose for the dose-, control-, and repeat trialswith exponential trend lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.4 The reduction of A) Diclofenac, B) Metoprolol and C) Oxazepam during the threetrials; dose-, control- and repeat trials of the pilot study and the reduction frommeasurements made on the full-scale facility. Sadly, oxazepam could not be mea-sured with the method used. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.5 The average for diclofenac, metoprolol and oxazepam remaining after ozonationagainst the ozone sensitive UVA left for the different trials and the new measure-ments as well as a linear trendline for the dose trial. The black arrow mark theoutlier. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.6 A) The measured ozone dose and the nitrite concentration over the day that the N-model was based on. B) The measured ozone dose compared to the dose predictedby the N-model during the days when the regulatory strategy was deployed. . . . 40

4.7 The measured ozone residual compared to the residual predicted by the MVA1-model and MVA2-model for hourly averages of 15 days during the summer, wherethe ozone dose was kept at 9.75˘0.3 mg/L. . . . . . . . . . . . . . . . . . . . . . . . 42

4.8 The measured ozone residual compared to MVA1-model residual for the data usedto construct the MVA1-model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.9 An overview of the Ozone Dose(sim) model . . . . . . . . . . . . . . . . . . . . . . 454.10 The ozone dose from the Ozone Dose(sim)-model and measured A) Nitrite, B)

Turbidity and C) COD(sim) variations for two days; 2014-07-19 and 2014-07-27,during the 15 day validation period used. . . . . . . . . . . . . . . . . . . . . . . . . 46

4.11 The ozone dose predicted by the Ozone Dose(sim) (in mg/L on primary y-axis)the turbidity (in FNU on primary y-axis) as well as the nitrite concentration (inmg/L on secondary y-axis) for three sets of input parameters (x-axis) from thenew measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

ix

List of Tables

3.1 Available data from measurements made and also measurements and on-line datafrom the pilot study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2 Parameters that were measured before and after the ozonation step. . . . . . . . . . 25

4.1 Daily averages of on-line data for the pilot study and the new measurements onthe ozone doses of 5, 6.5 and 8 mg/L. The change was calculated according toequation 3.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Parameters that influence the ozonation measured during the pilot study as wellas new measurements made on the ozone doses of 5, 6.5 and 8 mg/L. The changewas calculated according to equation 3.1. . . . . . . . . . . . . . . . . . . . . . . . . 29

4.3 The nitrite concentration before and after ozonation at the tested ozone doses. Thereduction was calculated according to equation 3.2. . . . . . . . . . . . . . . . . . . 30

4.4 UVA results from measurements before and after the ozonation on the full-scaleozonation.The ozone sensitive UVA left was calculated according to equation 3.3. . 30

4.5 Reduction of diclofenac and metoprolol during the new measurements on ozonedoses of 5, 6.5 and 8 mg/L. Sadly, oxazepam could not be measured with themethod used. The reduction was calculated accoring to equation 3.2. . . . . . . . . 34

4.6 Analysis of Variance and Model-, and Validation selection for MVA1 and MVA2. . 384.7 The coefficients received from MVA1 and MVA2 for the analyzed parameters. . . . 394.8 Nitrite and nitrate concentrations before ozonation for the different doses as well

as calculated quotients and averages. . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.9 Parameters that are expected to influence the ozonation measured during the pilot

study as well as the sampling days. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

x

1 Introduction

The trend for the annual sale of pharmaceuticals in Sweden is an increase in sales revenue aswell as in defined daily doses[1]. This trend is estimated to continue as the population andthe longevity increase [2] [3]. Pharmaceuticals improve the quality of life for the individualbut there are environmental consequences to take into consideration. Most pharmaceuticalresidues are excreted in urine and excrements before reaching the wastewater treatment plant(WWTP). [4] Nowadays, no purification step to remove pharmaceutical residues is present inthe WWTPs in Sweden [4, 5]. Therefore, the pharmaceutical residues reach the hydrosphere,where they have been shown to have negative effects on the aquatic life, for example caus-ing sterility in fish and thereby disrupting ecosystems [4, 6]. Since many aquatic organisms,including fish, have similar target molecules such as receptors and enzymes, there is an im-minent danger that the long-term effects of the pharmaceutical residues may impact humansas well. Moreover, the effects on humans of the pharmaceutical residues in the tap water, aswell as in seafood, are currently unknown, but might arise in the future. [5] By implementingan ozonation step for reduction of the pharmaceutical residues at WWTPs there will hope-fully never be an impact on humans and the observed negative effects on the aquatic life andenvironment will be minimized [4, 7].

1.1 Purpose of the study

Pharmaceutical residues in wastewater have been shown to have effects on the aquaticlife and together with a political interest from Linköping municipality, Tekniska Verken iLinköping AB (TVAB) investigated the possibilities to reduce the loads of pharmaceuticalresidues. In a pilot study in 2014, the method ozonation (treatment with ozone) was tested.With the positive results, TVAB decided to implement a full-scale facility at Nykvarnsverket,Linköping. However, as ozonation of wastewater is a fairly unfamiliar process and wastew-aters differ among the WWTPs there is no standard operating procedure. TVAB has a goal toreduce 90 % of the pharmaceutical residues, in order to eliminate the negative effects of phar-maceutical residues on the environment, in the wastewater using ozonation and to achievethis the right ozone dose must be used. [8] Underdosing may result in insufficient reductionof pharmaceutical residues while overdosing of ozone may result in more oxidation productsand unnecessary expenses. [9]

1

1.2. Objectives of the work

The purpose of this project was therefore to verify that the ozonation process works in full-scale, thereby verifying the pilot study and to investigate which parameters influence theozone consumption in order to formulate a model for the ozone demand.

1.2 Objectives of the work

The main objectives were to verify the pilot study and to find the optimal ozone dose to reacha reduction of pharmaceutical residues of 90 % at different conditions in Nykvarnsverket.This was achieved through measurements, by investigating which parameters influence theozone demand and the correlation between these parameters. Also, by developing and eval-uating possible models for the ozone demand based on data from the pilot study the mainobjectives were reached. To reach the main objectives they were divided into intermediateobjectives:

• Analyze and evaluate the pilot study

• Measurements on the full-scale facility of new parameters

• Perform measurements on the full-scale facility for different ozone doses

• Multivariate analysis to investigate the influence and correlation between different pa-rameters

• Understand the software to be used

• Construct and evaluate models for the ozone demand

1.3 Expected impact of the study

Pharmaceutical residues reach the hydrosphere through the outlet (outgoing water) fromWWTPs where negative effects have been shown in the aquatic life. The effects are pre-dominantly a reduced population and a different variety of species, which disrupts theecosystems. Further, antibiotic resistance might increase in bacteria, resulting in infectiousdiseases in humans that are difficult to treat. Additionally, the future effects on humans thatare exposed to pharmaceutical residues transferred to drinking water sources downstreamare unknown. The environmental issues as well as the risk of untreatable diseases arisingdoes not fit with the vision of TVAB. [10, 11]

TVAB works for a comfortable everyday life and a sustainable life cycle for the locals inforemost Linköping and Katrineholm. For instance, they work with waste, recycling, biogas,broadband, electricity-, grid and trading, district heating, remote cooling, drinking water andwastewater. TVAB strives to always be in the forefront of technological development and anexample is the pharmaceutical residue treatment from wastewater. A project was initiatedin 2014 and with a political interest from Linköping municipality, TVAB decided to investi-gate the possibilities of reducing the loads of pharmaceutical residues from wastewater atLinköpings largest WWTP, Nykvarnsverket. TVAB in collaboration with IVL Swedish Envi-ronmental Research Institute constructed a pilot study with an ozonation step in-between thebiological treatment and Moving Bed Biofilm Reactor (MBBR) step in the current process atNykvarnsverket. The results from the pilot study showed an average reduction of pharma-ceutical residues of about 90 %. Additionally, there was no added toxicity for the aquatic life,no formation of mutagenic by-products and no negative effect on the MBBR-process function. [8]

2

1.4. Delimitations

This pilot study was used as the basis for the design and implementation of the full-scale facil-ity, which is the first permanent, large-scale facility for reduction of pharmaceutical residuesin Sweden, inaugurated in September 2017. [8] Optimization, evaluation and verification ofthe process in full scale, will be performed under the start-up transnational EU-project, ClearWaters from Pharmaceuticals (CWPharma). [12]

1.4 Delimitations

The main constraint was that the full-scale ozonation process could only be operated duringthe last weeks of this thesis, which altered the initial plan. Therefore, time was lost reformu-lating the purpose. Instead of full-scale measurements on the ozonation step, data from thepilot study in 2014 together with data from new measurements were used to reach the objec-tives. Moreover, there was a limit in the amount of measurements that could be performedand parameters that could be analyzed due to the time restriction of 20 weeks and the re-sources available. Additionally, the time of measurements was restricted by the weather, assimilar conditions to those of the pilot study were preferred in order to obtain comparableresults and spring was very late this year. Moreover, validation of the model will be limitedwithin the scope of this project. Therefore, it will remain unknown if the model can predictthe ozone demand correctly and if the results of the pilot study are valid compared to full-scale data. A more extensive validation will have to be performed later after this thesis hasbeen completed.

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2 Theory and Methodology

2.1 Scientific background

Ozone has several different uses, for example to disinfect drinking waters and to removepharmaceutical residues from wastewater. Most pharmaceutical residues are excreted inurine and excrements and reach the wastewater treatment plant (WWTP) before they mightend up in the hydrosphere where they cause negative effects on the aquatic life and disruptecosystems. Thousands of pharmaceuticals are used in Sweden and many of these end up inthe WWTPs.[8]

Today, the conventional WWTPs do not have the ability to reduce the majority of the pharma-ceutical residues, including the three pharmaceuticals in focus during this thesis; diclofenac,metoprolol and oxazepam. [8] With a political interest from Linköping Kommun, TVAB in-vestigated the possibilities to reduce the loads of pharmaceutical residues with ozone to re-ceiving waters (mainly Stångån) at Linköpings largest WWTP, Nykvarnsverket. This wasdone through a pilot study in 2014.

2.1.1 Ozone

Normally talked about as the gas protecting earth against Ultra Violet-rays, ozone has beenused for disinfecting drinking waters for many years. However, recently a new applicationfor ozone, to remove pharmaceutical residues from wastewater, has been of interest. Duringozonation several parameters are known to influence the process and the ozone dose requiredto reduce the pharmaceutical residues present in the wastewater. [5, 13]

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2.1. Scientific background

Generating ozone

Ozone (O3) is an unstable gas, which must be generated at the point of use. The productionof ozone is performed by an electric current running through oxygen gas (O2), or air, whichleads to the formation of reactive oxygen intermediates. These intermediates then reactforming ozone and for every three oxygen, two ozone molecules are generated as follows;

O2 + electric energy Ñ 2 O2 O + 2 O2 Ñ 2 O33 O2 + energy Ø 2 O3

This reaction is reversible and when the unstable ozone decomposes to oxygen, energy isreleased. [5, 13]

Reaction mechanism of ozone

Ozone is one of the strongest chemical oxidants and can react either directly or indirectly witha variety of compounds. In the direct reaction ozone reacts with a compound, whilst in theindirect reaction ozone forms radicals which then react with the compound. [13]

Direct reactionIn the direct pathway ozone reacts with an unsaturated bond, due to the un-polar structure,leading to the bond being split. This is known as a Criegee ozonolysis reaction and allowsthe cleavage of alkene double bonds with ozone. Ozone will react quickly with aromaticcompounds like hydroxyl groups, since they carry electron supplying substituents, as well asother electron rich moieties such as tertiary amines and thioethers. [13, 14]

Indirect reactionThe indirect pathway with radicals is very complex, however the reaction can be dividedinto the three steps; initiation, radical chain and termination. The radicals will react withadditional compounds to the direct reaction, such as alkanes and amides. The reaction stepsare influenced by substances that act as initiators, promoters or scavengers. Initiators, forexample the hydroxide ion, hydrogen peroxide or bivalent iron (Fe2+), are chemicals whichinitiate the ozone decay and the formation of the radicals. Promoters, for example humicacid, primary alcohols or secondary alcohols, are substances which promote the radical chain.Scavengers, for example hydrogen carbonate, carbonate ion or phosphate, are substanceswhich do not produce radicals required in the radical chain. Therefore, the radical chain isterminated or inhibited. Based on the presence of substances within these groups as well asconditions such as pH or temperature, the direct or indirect reaction will dominate. As theindirect reaction is non-specific and faster than the direct reaction, this will influence howfast the reaction for the compound with ozone is, as well as the degradation efficiency fordifferent substances. [13, 14, 15]

Ozone and Aromatic CompoundsSometimes ozone reacts directly with an aromatic organic compound, leading to the forma-tion of radicals. As mentioned ozone reacts with electron rich moieties and the radical isformed through an electron transfer from the aromatic organic compound to ozone. As ozonereacts with the electron rich moieties new electron rich sites are generated, leading to radicalsbeing continuously formed when ozone is present. The new sites are generated in the formof phenols, which result either from a direct reaction of ozone with the aromatic organic com-pound or an indirect reaction between the compound and the radical. The indirect reactionrequires the presence of oxygen, which is available during ozonation. [13]

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Parameters that influence the ozonation of pharmaceutical residues

During ozonation several parameters influence how much ozone that is required in order tosuccessfully reduce the pharmaceutical residues in the wastewater. Turbidity, Dissolved Or-ganic Carbon (DOC), Chemical Oxygen Demand (COD), nitrite concentration, conductivity(concentration of ions) and Ultra Violet Absorbance (UVA) are amongst the parameters thatare known to influence the ozonation process and the ozone demand.

TurbidityTurbidity, the measure of cloudiness in the water, is measured by applying a light beam toa water sample and measuring the intensity of the scattered light at a 90˝ angle to the lightbeam. A higher intensity corresponds to a higher turbidity and a higher turbidity is expectedto result in a higher ozone demand. Based on the frequency of the light, different units areused to measure turbidity, for example Formazin Nephelometric Unit (FNU) measured withan infrared light. Turbidity covers several other parameters, both organic and inorganic, suchas DOC, COD and nitrite. However, as turbidity depends on the shape, size and refractiveindex of the particles, observing a direct correlation between turbidity and the weight of thesuspended matter in the water is difficult. Therefore it is of interest to measure additionalparameters even though turbidity can be used as an indication for an increase in suspendedmaterials and thereby an increased ozone demand. [16, 17]

DOC and CODDissolved organic matter, commonly measured as DOC, largely determine the stability ofozone in wastewater, where an increased DOC concentration corresponds to a reduced sta-bility of ozone. The nature of DOC influence the reaction rate and thereby ozone lifetime,which in turn affect the reduction of pharmaceutical residues. This reduction depends onthe lifetime of ozone in the wastewater, where a longer contact time between pharmaceuticalresidues and ozone leads to a better reduction. Ozone reacts with the electron rich moietiesof DOC and the reaction leads to the formation of radicals during ozonation. As the reactionwith radicals is faster than the direct reaction with ozone the formation of radicals fromDOC reduce the stability and lifetime of ozone in the water. This means that an increasedDOC concentration shortens the lifetime of ozone in the wastewater, thereby influencing thereduction of pharmaceutical residues and leading to more ozone being required at higherDOC concentrations. [13, 18, 19]

Another parameter which influence the stability of ozone is carbonate alkalinity, which act asa scavenger for radicals. Other parameters, mainly DOC and COD can also act as scavengers.This means that the ozone stability and lifetime decrease and thereby also the reductionefficiency for pharmaceutical residues. [13, 18, 19]

COD can also be used to measure organic content and has been correlated with the ozonedemand where a higher COD concentration corresponds to a higher ozone dose require-ment. COD is reduced faster than DOC as the oxidation of an organic compound initially de-crease the COD concentration whilst DOC decrease after the organic compounds have beenmineralized. Hence, COD concentrations are affected more rapidly by ozonation than DOCconcentrations. Both DOC and COD can be determined by adding a strong oxidant, usu-ally dichromate or permanganate, and measuring the amount required for oxidation of thesample. If permanganate is used higher concentrations for COD are determined as this is astronger oxidant. [13, 18, 19, 20]

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Nitrite concentrationNitrite is generally removed by biological processes in WWTPs. However, if this removal isincomplete ozone reacts quickly with nitrite present in the water. Hence, at a higher nitriteconcentration more ozone would be required to oxidize nitrite to nitrate. A high resolutiondual beam spectrometer, using UVA, is required to measure the nitrite concentration, where apeak in absorption is observed around 210 nm. However, at this wavelength there is an over-lap with nitrate as well as some organics. This makes on-line measurements of nitrite com-plicated, especially with the low concentrations normally detected in WWTPs of 0.1 mg/L.Therefore, nitrate is used in on-line measurements since there is a known correlation betweennitrate and nitrite, where the trends mirror each other. [13, 17]

ConductivityInorganic compounds, like ions, can be oxidized by ozone. This means that higher ionic con-centrations correspond to a greater ozone demand. Conductivity can be used to measurevarious ionic concentrations in one parameter, for example iron, chloride and bromide. Theunit for conductivity is usually microsiemens/meter (mS/m). Changes in conductivity nor-mally arise from nitrogen removal in biological treatment. Conductivity is easily determinedthrough measuring the changes in salt concentrations on-line with for example electrodes.[13, 21]

UVAUVA does not directly influence the ozone demand but can be correlated to other parame-ters such as DOC and COD. A higher UVA corresponds to higher concentrations of organicmaterials as this usually is composed of aromatic compounds, which absorb UV-light at 254nm. As higher DOC and COD concentrations correspond to a higher ozone demand thereis an indirect correlation where a higher UVA corresponds to a higher ozone consumption.Additionally, UVA has been tested as a control strategy for the ozone dose required to reducethe concentrations of pharmaceutical residues at different conditions. The results showedthat UVA, or UV reduction meaning the difference in UVA before and after ozonation, is veryefficient as the basis for a regulatory strategy. [9, 13, 17, 18]

Other ParametersIn addition, other parameters, such as pH and temperature influence the ozonation. The for-mation of radicals increase at a higher pH and at values pH>10 only the indirect pathwayoccurs. The efficiency of a biological treatment step is influenced by temperature, where ahigher temperature increase the reduction of organic and inorganic materials. Hence, if abiological step is present before the ozonation a higher temperature would reduce the ozonedemand as the organic and inorganic material is reduced. In addition to reducing the con-tent of organic and inorganic compounds in the water, ozonation will reduce pharmaceuticalresidues in the wastewater, thereby preventing them from reaching the environment. [13, 18,22]

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2.1.2 Pharmaceuticals

Pharmaceutical residues that are released into receiving waters have a negative effect on theenvironment and the aquatic life. Therefore, a list of substances to be monitored have beencompiled for pharmaceutical in different groups such as anti-inflammatory drugs and antide-pressants. [22, 23]

Pharmaceuticals to be monitored

In 2014 a list of 22 pharmaceuticals to be monitored in Sweden was compiled. In addition, EUhas a watch list of substances which must be monitored that were included in this list. In thisthesis the focus lies on three of the listed substances; diclofenac, metoprolol and oxazepam.[23, 24]

DiclofenacIn Sweden there are national limits for substances that are hard to degrade at WWTPsand diclofenac is the only environmentally hazardous substance which exceeds its limit.As diclofenac is easily accessible, being a non-prescription drug, the use of this substanceis very high. Additionally, diclofenac is the active substance in Voltaren, which is fre-quently marketed leading to a further increase in use. Diclofenac is a non-steroidal anti-inflammatory drug which inhibits cyclooxygenase resulting in a reduced prostaglandin for-mation. Prostaglandin cause pain and inflammation and the use of diclofenac reduces theseeffects. Diclofenac is taken between 1-4 times of the day depending on the form, usually inpills or a gel. As the pharmaceutical is non-prescription and only used when needed thereare no recommended dosing intervals or times of day to take diclofenac. The structure ofdiclofenac, see figure 2.1, contain an ozone reactive phenyl group indicating that ozonationwould reduce diclofenac. [25, 26]

MetoprololMetoprolol is a β-blocker which binds to β-receptors, thereby inhibiting the hormonesadrenaline and noradrenaline from binding and exerting their full effect. Adrenaline andnoradrenaline are released into the blood during stress, physical or psychological activityand then bind to β-receptors in the heart and blood vessels, leading to an increase in bloodpressure. By inhibiting the binding of adrenaline and noradrenaline to β-receptors the heartrate slows as well as the pumping force thereby reducing the blood pressure and preventinghypertension. Metoprolol is a commonly used β-blocker that is selective for the β-receptorsin the heart. Pills with metoprolol are prescribe and usually taken 1-2 times a day, but thedose can be individually adapted. The structure of metoprolol, see figure 2.1, contains bothan aliphatic chain and an aromatic ring, which are ozone reactive groups, indicating that theozonation should be efficient in reducing the metoprolol content in the water. [27, 28]

OxazepamOxazepam is an antidepressant which affects the GABA-system in the brain. GABA-steroidsare valium like substances that bind to the GABAA receptor, which is also the target receptorfor alcohol and benzodiazepines, like oxazepam. The binding of GABA-steroids has sedative,anti-depressing and relaxing effects, which are reinforced by oxazepam. As a prescriptiondrug, oxazepam is usually used once a day, but can be taken up to four times a day if needed.The structure of oxazepam, see figure 2.1, lack ozone reactive groups and the aromatic ringsare deactivated by electron negative groups, causing a low ozone reactivity. [8, 29]

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Figure 2.1: The structure of A) Diclofenac, B) Metoprolol and C) Oxazepam

Effects of pharmaceuticals

Pharmaceuticals residues that are excreted in human urine or feces reach the WWTPs andwhile some substances are removed by the current process, many pharmaceutical residuesare released into receiving waters where they have a negative effect on the environment,since they still have a pharmacological effect. [22]

Aquatic lifeThe effects of waterborne diclofenac on rainbow trout was investigated and the study showedthat the exposure to diclofenac caused tissue damage at concentrations of 1 µg/L. Damagesinclude inflammation, hyperplasia and necrosis in the kidney. Exposure to diclofenac alsoaltered mRNA expression and could have a significant impact on the health of fish long-term[30]. Studies have also shown that diclofenac can cause DNA damage which leads to im-munosuppression as well as genotoxicity in fish [31]. Additionally, diclofenac concentrationslower than 1 µg/L can cause liver damage in rainbow trout [32]. In Stångån a diclofenacconcentration of 0.48 µg/L was calculated based on measurements during the pilot study. [8]

In a study on zebra-fish embryos metoprolol exposure resulted in scoliosis, heart abnormal-ities and growth retardations at doses of 25 mg/L. However, these effects were observedfor metoprolol doses higher than what has been observed in surface water, of 0.2 µg/L andtherefore seem like an insignificant risk for fish. Although in more sensitive aquatic organ-isms, green algae and crustaceans, metoprolol exposure at lower doses have shown negativeeffects, for example an effect on heart rate for crustaceans [32]. Additionally, metoprolol isconsidered as toxic for aquatic life and negative effects on the growth of green algae havebeen reported [33]. In Stångån a metoprolol concentration of 3.09 µg/L was calculated basedon measurements during the pilot study. [8]

The treated effluent from a Swedish WWTP had an oxazepam concentration of 72 µg/L ina study that investigated the alterations in the behavior of perch after oxazepam exposure.The study showed a bioaccumulation of oxazepam in muscle tissue and significant effects

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on the behavior of perch. Treatment with a low dose of oxazepam, 1.8 µg/L, resulted inasocial and more active individuals, compared to the untreated fish. Additionally, oxazepamincreased the feeding rate of fish which might have effects on the ecosystem in the future. [34]A oxazepam concentration in Stångån of 0.3 µg/L was calculated based on measurementsduring the pilot study. [8]

Environmental effects of sex hormones on the aquatic life are well known, for example thefeminization of male frogs and fishes by the hormones, ethinyl estradiol, estradiol or a gestrel,in the contraceptive pill [31, 35]. A study reported that the exposure to levonorgestrel at anearly stage in life of frogs had several effects that manifested themselves in adults. Femalefrogs exposed to levonorgestrel throughout life became sterile, as a result of severe disrup-tion in the ovary and oviduct development. Additionally, levonorgestrel has been shown tobioaccumulate in fish and exposure resulted in inhibition of the egg-laying for female adultfish [36]. In a study of zebrafish exposed to progesterone or norgestrel a disruption in sexdifferentiation was observed. For progesteron the proportion of females increased, at a doseof 63 ng/L, while an increase in the proportion of males was observed for norgestrel at 34 and77 ng/L. It is thought that the exposure to these compounds alter the transcriptions of genesrelated to the synthesis of sex hormones and thereby the levels of sex hormones in zebrafish.[37] Levonorgestrel was calculated to a concentration of <0.432 µg/L in Stångån based onmeasurements during the pilot study. [8]

Resistance in bacteriaThere is a concern that bacteria resistant to antibiotics are formed in WWTPs and recivingwaters as well as a concern that the effluents from WWTPs contain antibiotic concentra-tions close to effect levels. Resistant bacteria have been found in sludge from the wastewatertreatment, these bacteria were resistant to various antibiotics at high levels and the numberof resistant bacteria increased in the summer. Additionally, increased resistance in bacte-ria after long-term exposure to subtherapeutic antibiotic concentrations have been reported.Also, microorganisms exposed to antibiotics that become resistant can transfer their genes topathogenic bacteria, which can result in infections in humans that are difficult to treat. [10,31]

Other organismsWhen vultures were found to be endangered due to the consumption of diclofenac fromtheir food, dead cows treated with diclofenac in India, concerns about pharmaceuticals inthe environment arose. The decline in vulture population with 95 % in the 90’s resulted fromrenal failure and visceral gout, which were the effects of diclofenac consumption. The sourceof diclofenac might also be from the water source, although the concentrations of diclofenacin water is low and bioaccumulation does not occur in vultures, making this a negligiblesource of diclofenac compared to the dead cows. [38]

Humans consume pharmaceutical residues through water and food, mainly fish at low con-centrations and so far, no effects have been observed. However, effects of pharmaceuticalresidues on humans and animals have not been investigated for a longer time period. Also,the exposure to multiple pharmaceuticals have not been investigated. Additionally, the fre-quent introduction of new drugs results in their being substances with effects that are yet tobe determined. Moreover, the effects of exposure to pharmaceutical residue might have adifferent impact due to individual variations in sensitivity as well as the sensitivity of certaingroups, for example the elderly. [22]

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2.1.3 Wastewater treatment

The water from the sewage system, industries as well as surface water must be purified toavoid negative effects on the environment. This is performed by WWTPs, using mechanical,biological and chemical treatment techniques. In Linköping, TVAB is responsible for thewastewater treatment and for making sure that water being returned to the environmentmeets the set requirements. [5, 39, 40]

Nykvarnsverket

The WWTP of TVAB in Linköping, Nykvarnsverket, was built in 1952 and has since under-gone several changes, the latest being the addition of an ozonation step, for pharmaceuticalresidue treatment, in 2017. Today, Nykvarnsverket meets the requirements for water beingreturned to the environment and with the ozonation this will be the case in the future aswell. In the process, see figure 2.2, the outgoing water is not the only product from theWWTP. Additionally, biogas used in buses for public transport and sewage sludge used asfertilizer in agriculture, is produced at Nykvarnsverket. However, the focus in this report isthe wastewater side of the process and especially the ozonation step. [39, 40]

Untreated wastewater poses several issues, mainly littering the recipient, spreading disease,eutrophication of waters, spreading environmentally hazardous substances and the latestconcern with the effects of pharmaceutical residues on aquatic life. This is prevented bythe wastewater treatment process at Nykvarnsverket, where the main steps are; [5, 39, 40]

1. Screens

2. Grit chamber

3. Aeration

4. Primary clarifier

5. Biological treatment

6. Ozonation

7. MBBR

8. Secondary clarifier/chemical treatment

The wastewater treatment process starts in a general way with mechanical treatment usingthe first two steps; screens and grit chamber. The screens are used as a first step to removelarger objects, such as plastics, toilet paper and ear swabs preventing the littering of the re-ceiving water, Stångån. The water then proceeds to the grit chamber, where heavier particlessuch as sand and coffee-grounds sediment. In the inlet to the grit chamber iron sulfate isadded as a precipitation chemical. The iron is oxidized, from bivalent to trivalent, in the gritchamber, as well as in the following aeration step, leading to the formation of flocs. Theseflocs are composed of the iron, phosphorus and organic material, which sediment in thefourth step, the primary clarifier. Cat- and anionic polymers are added before this step toenhance the effect of the iron and to ensure that phosphor is removed from the water as thisis a major source in eutrophication. The sedimented flocs form sludge, which is then usedfor biogas production. [5, 39, 40]

The biological treatment is the fifth step, where organic materials and nitrogen are removedby microorganisms. The aeration in this step is intermittent, making the environment aerobicand anoxic in relation to when the aeration occurred. The organic material is either degradedto carbon dioxide and water or converted into new biomass. Additionally, remaining iron

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is oxidized, form flocs and sediment in the biological treatment and the sludge formed inthis step is returned back into this step to ensure that the microorganisms remain in theprocess. Also, nitrogen is converted to nitrogen gas in two steps by the microorganisms inthe biological treatment step. Nitrification is the first step, where nitrogen in the form ofammonium is oxidized to both nitrate and nitrite by bacteria in an aerobic environment.Then denitrification, where the both nitrate and nitrite are oxidized to nitrogen gas by othermicroorganisms in an anoxic environment, is the second step. The nitrogen gas is releasedinto the atmosphere, which already has a high nitrogen content of almost 80 %. [5, 39, 40]

After the biological treatment the newest process step, which will be covered more in detailbelow, is to take place. The pharmaceutical residue treatment with ozone will remove an-tibiotics, antidepressants, painkillers as well as bacteria resistant to antibiotics once running.This is the first permanent full-scale facility in Sweden and was inaugurated in the fall of2017. [39, 40]

After ozonation is the MBBR step, with carriers coated in biofilm. In this step nitrogen is con-verted to nitrogen gas in two steps by the microorganisms in the biofilm, like in the biologicaltreatment step. Nitrification and denitrification occur in separate tanks which are aerobicand anoxic, respectively. Ethanol as a source of carbon and phosphoric acid as a source ofnutrition for the microorganisms in the biofilm are added between the nitrification and deni-trification. In the last step, the secondary clarifier or chemical treatment, aluminum chlorideis added to precipitate phosphorus, which is done as before with the formation of flocs whichthen sediment and form sludge. After this step the water exits the process and goes into therecipient, Stångån. [5, 39, 40]

Figure 2.2: The wastewater treatment process at Nykvarnsverket.

Pharmaceutical residue treatment

The first permanent full-scale ozonation facility for continuous use was constructed in 2017based on successful results of a pilot study at Nykvarnsverket in 2014, see section 2.1 below.The goal with adding ozonation in the process is to achieve a 90 % reduction of pharmaceuti-cal residues in the outgoing water, thereby preventing the negative environmental effects.[39,40] Ozone is generated by running an electric current through liquid oxygen and the ozoneinjector as well as the water basin have been designed to ensure an effective mass transfer, seefigure 2.3. A high Mass Transfer Efficiency (MTE) is obtained by having an efficient mixingof ozone with water. Part of the biologically treated water from the previous step is mixedwith ozone, whilst the remaining water enter the basin from the bottom. In the injector,placed in the top of the water basin, the speed of the incoming water is increased, creating a

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vacuum. This vacuum results in the ozone gas being drawn into the water. As the water andozone exits the injector the speed is reduced and mixing blades create ozone microbubbles.Since these bubbles increase the contact surface between ozonated water and the biologicallytreated water, the mass transfer becomes more efficient. Additionally, a radial diffuser placedat the outlet of the injector is used to distribute the water-ozone mixture efficiently and alsoto create more microbubbles. This gives a larger contact surface which is prerequisite for anefficient mass transfer of ozone from the gas- and water phase. By having a large contactsurface the process can operate more effectively, as ozone will react with the water that itcomes into contact with.

The water basin was designed to ensure that the water moves homogeneously through thereactor so that no dead zones or short circuit streams forms. Additionally, the contact timebetween ozone and water is evenly distributed in the water volume with the design, result-ing in more efficient reduction of pharmaceutical residues in the water. Both on-line andoff-line measurements can be made at certain sampling points to monitor the process. Theozonation was placed after the biological treatment to avoid that ozone reacts with organicmaterials, which can easily and more cheaply be degraded by the microorganisms. After thepharmaceutical residue treatment, the MBBR-step has been shown to be an efficient methodfor removing by-products formed by the ozonation. Hence, the ozonation was placed here,making it the sixth of eight steps in the process at Nykvarnsverket. [5, 39, 40, 41]

Figure 2.3: The ozonation process with ozone generation from liquid oxygen and on-linemeasurement points (On 1, On 2 and On 3).

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2.1.4 Pilot study

With a political interest from Linköping Kommun, TVAB investigated the possibilities to re-duce the loads of pharmaceutical residues to receiving waters (mainly Stångån) at Linköpingslargest WWTP, Nykvarnsverket. This was done through a pilot study in 2014.

Background

Over a thousand different pharmaceuticals with different active substances are used in Swe-den. These pharmaceutical residues reach the WWTPs via urine and excrement and mightthen end up in the hydrosphere. Analgesic and anti-inflammatory pharmaceuticals likeparacetamol, ibuprofen, naproxen and diclofenac are the dominating pharmaceuticals in theinfluent. However, some of these pharmaceuticals, especially paracetamol and ibuprofen, arereduced effectively in the conventional WWTPs today. In the outgoing water it has been ob-served that furosemide, metoprolol, atenolol and diclofenac are not reduced in the WWTPs.Additionally, pharmaceuticals for the central nerve system, like the substance oxazepam,have a limited reduction in the WWTP.

Current studies have shown no effect of the pharmaceutical residues on humans. How-ever, it has been shown that the pharmaceutical residues in low concentrations (ng-µg/L)have negative effects on the aquatic life and environment, for example causing sterility andpersonality disorder in fish. Moreover, the microbial ecosystem may be disrupted, whichin turn may affect higher ecosystems. Although the debate regarding the pharmaceuti-cal residues was new and knowledge limited, the politicians in Linköpings Kommun hadan interest to investigate whether a reduction of pharmaceutical residues in Linköpingswastewater was possible or not. TVAB in collaboration with IVL Swedish EnvironmentalResearch Institute then decided to construct a pilot study at the biggest WWTP in Linköping,Nykvarnsverket. The goal with the pilot study was to investigate whether ozonation wouldbe a possible method to use in order to reduce the pharmaceutical residues in the wastewater.

Ozonation and adsorption with active carbon as purification techniques were initially of in-terest for TVAB. Both techniques have a relatively similar pharmaceutical residues reductioncapacity, but an evaluation witch focus on ecotoxicity, nitrogen-, phosphorus-, BiochemicalOxygen Demand (BOD) reduction and bacterial reduction as well as energy costs resulted inthat the ozonation technique was deemed to have the most beneficial prerequisites and to bethe best fit for TVAB.

Before the ozonation process was installed, the contents of pharmaceutical residues was mea-sured after the biological treatment process, which gave a good indication of the amount ofpharmaceutical residues that end up in the recipient. TVAB found 28 substances, which waslisted in a priority list with different levels of risk (high risk, moderate risk and low risk).There were 5 substances with high risk in the priority list, including oxazepam and metopro-lol. Diclofenac on the other hand, was assessed as a moderate risk. [8]

Method

The pilot plant was set-up at Nykvarnsverket in Linköping and the pilot study was carriedout over one year, where the ozonation trial period was about 5 months including a controltrial and a repeat trial. The ozonation trial period included several studies and measure-ments, including daily variation mapping, ecotoxicological- and dose response studies forozonation.

The reaction tanks were two series bubble columns with a volume of 0.115 m3 each, whereozone was added from the bottom of the columns. 12 different doses of ozone were tested

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(where 1.8 mg/L was the lowest ozone dose and 23.1 mg/L the highest ozone dose). Theretention time was held constant at 11 minutes and the flow was 1.5 m3/h for all doses tested,with the exception of the two lowest ozone doses, where the flow was set to 1.7 m3/h.

During the trial period, samples were taken before, during and after ozonation. Additionally,samples were taken during and after the MBBR step in order to analyze the by-product forma-tion and to evaluate how the ozonation step affects the process in general. The samples wereanalyzed in laboratories, both at TVAB but also at IVL Svenska Miljöinstitutet, Swedish Uni-versity of Agricultural Sciences (SLU) and Toxicon AB. Furthermore, parameters like UVA,temperature, pH, flow, nitrate and turbidity were measured on-line. [8]

Results

In summary, TVAB observed a significant reduction of the pharmaceutical residues whenthe ozonation process was operated. Toxicity for the aquatic life did not increased and noformation of mutagenic by-products or increased gene toxicity could be observed. Moreover,there were no negative effect on the denitrification in the MBBR-step caused by the ozonation.

Daily variation of the water flow into the WWTP as well as daily variation of the pharma-ceuticals were observed. For some groups of pharmaceuticals, like antibiotics, which onetypically take twice a day, there was a dilution during the morning due to the increasedwater flow. Comparable, for pharmaceuticals which one typically take once a day, like an-tidepressant pharmaceuticals, no variation in concentration could be seen. Moreover, it wasobserved that the flow was lower during the summer period, probably due to the vacationwhen many students temporary move and several big industries in Linköping shut down.This means that a seasonal variation in flow occur as well.

The daily and seasonal variation of the flow in to the WTTP affected the ozone residual, i.e.remaining ozone in gas phase. A higher flow of the water gave a shorter retention time inthe biological treatment step. Furthermore, the shorter retention time resulted in an increaseof organic materials in the water, hence more ozone was consumed which lead to a decreasein the ozone residual.

A correlation between reduction of pharmaceutical residues and ozone sensitive UVA wasobserved, i.e. a higher reduction of pharmaceutical residues resulted in a lower UVA. Onthe other hand, there was a lack of data and uncertainties in analyzes during the pilot study.It can therefore be beneficial to evaluate UVA in correlation with pharmaceutical residuesfurther.

Nitrite concentration was another parameter which was of interest during the pilot study.Nitrite is oxidized to nitrate in presence of ozone, which means that nitrite concentrationdirectly affect the consumption of ozone. The highest and the lowest nitrite concentrationdiffered with 0.5 mg Nitrite Nitrogen(NO2-N)/L, which corresponded 1.7 mg/L increasedozone demand.

All pharmaceuticals measured were reduced in the ozonation process. After the ozonation(with a ozone dose of 5 mg/L), only one of the substances, oxazepam, was assessed as a highrisk. Metoprolol on the other hand, end up as a moderate risk. Diclofenac had a risk factorlower than 0.01 and was therefore removed from the priority list post ozonation. Based onthe result, an ozone dose of around 5-8 mg/L depending on externals seems preferable forTVAB in order to reach 90% reduction of pharmaceutical residues which would minimize therisk of negative effects on the environment. [8]

15


Possible Improvements

Due to the results of this pilot study, it is obvious that the ozonation is a complex processwith many parameters to take in consideration. The wastewaters differ among the WWTPsand there are consequently no general guidelines to follow. Underdosing may result ininsufficient pharmaceutical residues elimination while overdosing of ozone may result inmore oxidation products and unnecessary expenses. It is therefore important for TVAB tofind the ideal dosage of ozone, preferable using a model and/or automatic control strategies.[9]

Moreover, TVAB together with 14 other organizations, participates in an EU-project calledCWPharma, where the purpose is to reduce the active pharmaceutical substances in the BalticSea. With financing from EU, the contributions have the opportunity to test and evaluatepurification techniques that reduce the pharmaceutical residues from aquatic environment.In TVABs case, they have the opportunity to optimize the ozonation process at Nykvarnsver-ket, Linköping. [12]

Constructing a model or control strategies to the full scale-process from the historic data ofthe pilot study can bring difficulties due to the difference in conditions or constructions. Inthis case, the ozone reactor in the pilot facility had a size of 2 x 0.115 m3 comparable with the600 m3 in the full-scale facility. Moreover, the flow was held constant in to the ozonation stepduring the pilot trial period, which is not the case in the full-scale facility. Another aspectto take in consideration is the population growth in Linköping during the last four years ofapproximately 7000 inhabitants. [42]

16

2.2. Methodology

2.2 Methodology

A timeline illustrating how the work was carried out can be seen in figure 2.4. The initialphases during this thesis were a pre-study and a literature study. This was followed by themultivariate analysis and model construction based on different data from the pilot study.Measurements were performed on the wastewater in the full-scale facility in order to verifythe results from the pilot study. Moreover, measurements were performed to find new ozoneconsuming parameters. Further, the different models that were constructed in the multivari-ate analysis were evaluated in order to find the best one. The chosen model was then testedin MATLAB with a test file, at the same time as the pilot study was verified.

Figure 2.4: A timeline illustrating how the work was carried out.

2.2.1 Pre-study

During the pre-study the focus was on planning the project by setting goals, determiningactivities to be performed in order to reach the goals and allocating time for each activity.Information was gathered by talking to internal as well as external stakeholders. Internally,meetings with supervisor and the laboratory at TVAB were held in order to decide whichmeasurements that were of interest, but also when they should be performed. Moreover,meetings with external stakeholders around north Europe (Sweden, Germany and Denmark)were held to exchange knowledge and experiences within ozonation of wastewater. Ad-ditionally, a participation at the NAM-conference (Nationella Konferensen Avlopp & Miljö)gave interesting inputs and ideas to this thesis. The result from the pre-study was a planningreport, which contained the objectives, goals, boundary conditions, methods, activities, mile-stones, a gantt chart and a short theoretical background. The plans in this report has sincebeen revised with the original plan and goals in mind.

2.2.2 Literature study

To receive information about ozonation in WWTPs as well as different pharmaceuticals, a lit-erature study was carried out. Additionally, the pilot study was thoroughly studied in orderto get a deeper understanding of the conditions for Nykvarnsverket. Moreover, the achievedresults from the pilot study were essential to complete the main goals in this thesis. The pilotstudy results were compiled in a report, which was received from TVAB. The aim with theliterature studies was mainly to find information about the parameters that were found to beinteresting in the pre-study. Additionally, it was of interest to find other WWTPs that haddone similar projects. Literature received from Linköpings University library database andTVABs internal system were the most used where ebooks, reports and articles were of interest.The outcome of the literature study was an understanding of how the conditions at differentWWTPs affect the ozonation process and that there is no standard operating procedure. [13,

17

2.2. Methodology

18, 43] Moreover, the degrading ability for the pharmaceuticals of interest (diclofenac, meto-prolol, oxazepam) were investigated and the phenyl groups, aliphatic chain and aromaticrings were found to be the reactive groups within the structures. However, oxazepam hasno ozone reactive groups and the aromatic rings are deactivated by electron negative groups,making oxazepam hard to reduce from the wastewater. [8, 13]

2.2.3 Main activities

The knowledge and results from the pre-study and the literature study gave an idea aboutwhich parameters that could be of interest when the main activities were planned. However,the plans were revised during the project since problems with the ozonation arose. To reachthe goals for this thesis, a multivariate analysis and measurements on the full-scale facilitywere decided to be the main activities. The multivariate analyzes were performed to inves-tigate the influence and correlation of various parameters that were found to be of interestduring the literature study. To verify the results from the pilot study, new measurementswere made on the full-scale ozonation.

Multivariate analysis and regression

Before the multivariate analysis, the data from the pilot study was sorted. Days when it washeavy rain, problems with the equipment due to a thunderstorm or a power outage wereexcluded. Moreover, the data was sorted based on dose-, control-, and repeat trials. All dataused was an hourly average of the standard data. The data for the regression fitted line plotwas based on data from an ideal day, meaning when the behavior of nitrite and the ozoneresidual was as expected according to literature. [13]

The multivariate analyzes were conducted in Minitab, which was received from LinköpingUniversity. Minitab was selected as it is a user friendly and easily manageable statisticalsoftware [44]. Several functions were used, like Principle Component Analysis (PCA), PartialLeast Square (PLS) and Regression - fitted line plot. The PCA was used in order to identifyoutliers in the data. PLS was used to identify which predictors (parameters) that correlatedto the response variable (ozone residual), but also how much the parameters correlated toeach other and the response variable. [44, 45] The Regression function was used in order tofit data to a cubic regression line.

The results from the multivariate analysis were three different models which were evaluatedand the best model was tested in MATLAB.

Measurements on full-scale facility

Measurements were performed on the full-scale facility in order to verify the pilot study andfind new parameters that affect the ozone consumption and thereby the ozone demand.

To verify the pilot study, the conditions needed to be similar to those of the pilot study,meaning during spring temperatures and no heavy rain or snow melting. The amount ofpharmaceutical residues before and after the ozonation process were investigated to confirmthat all of the substances were reduced to 90% in the full-scale facility. Even though it wasonly possible to measure daily average on other parameters like DOC and suspended solids,these results was compared to the results from the pilot study. The effects on these parame-ters from the different conditions in the full-scale facility compared to the conditions duringthe pilot study were investigated.

Moreover, new interesting parameters like COD, Fe2+ and conductivity were measured.These were sampled before the ozonation step and after the biological treatment in order to

18

2.2. Methodology

see whether these parameters correspond to a part of the ozone consumption. Unfortunately,these parameters could not be included in the model construction due to the lack of data.However, the results may be valuable for TVAB in the future.

Depending on which parameter that was measured, the different samples were send to dif-ferent laboratories. The sample for COD analysis was sent and analyzed by SYNLAB inLinköping. Moreover, the pharmaceutical residues were sent and analyzed by a laboratoryat Aarhus University, Denmark. The rest of the samples were analyzed by the laboratory atTVAB. The results were compared with the result from the pilot study in order to verify theozonation process and thereby the pilot study.

2.2.4 Validation

An important part in this thesis was to find a model that could predict the ozone demandbased on different conditions in the wastewater. Alternative models that could predict theozone residual were constructed in order to find the best one. The models were validated bycomparing real data with data received from the models and the best model was selected.The data used was, as mentioned above, from the pilot study due to lack of data from thefull-scale facility. The result from the validation was the selection of the model which fittedthe validation data best.

2.2.5 Verification and testing

The pilot study was verified by comparing results from the new measurements with pilotstudy results. In parallel, the chosen model was then tested in MATLAB with a test file.

Verification

To verify the pilot study, the new results were compared to the results from the period whenthe pilot study was carried out. Unfortunately, the ozonation could only be run for approx-imately three hours before a filter was clogged. The comparison was therefore limited andonly few of the parameters could be included in the comparison. On the other hand, mea-surements of the pharmaceutical residues could be done and thereby it was possible to seeif the ozonation process in the full-scale facility worked as expected. The reduction of phar-maceutical residues back in 2014, during the pilot study, was compared with the reductionduring the spring of 2018. The results from this made it possible to verify the pilot study andthe full-scale facility.

Testing

The best model was tested in MATLAB to confirm and evaluate the model. A script wasconstructed in order to calculate different ozone doses depending on different conditions.A test file with different conditions for the parameters included in the model was run. Theozone dose was set to have a lower limit of 4 mg/L and a higher limit of 8 mg/L in order toavoid the consequences for over-, and underdosing ozone. [9] Moreover, a start dose was setto 6 mg/L, right in between these two limits.

19

2.3. Models

2.3 Models

For the multivariate analyzes both PCA and PLS were used.

2.3.1 PCA

PCA is an analysis where the aim is to identify a smaller number of uncorrelated variables.However, in this thesis, a PCA was used in order to identify outliers in the data. Pointsover a calculated reference line were classified as outliers and thereby excluded in furtheranalysis. The outliers were calculated and visualized in the software (minitab), specified inMahalanobis distance (MD) which is the most common way to identify outliers. The MDbetween two objects was defined as follow [46];

d(Mahalanobis) =b

(xi´x)1ˆ(xi´x)C , where

$

’

&

’

%

xi = an object vectorx = arithmetic mean vectorC = sample covariance matrix

2.3.2 PLS

PLS is a statistical technique where it is possible to investigate the relationship betweena response variable and different predictors in a multivariate dataset [45]. The algorithmthat is used is a nonlinear iterative partial least square (NIPALS) algorithm. The number ofpredictors is reduces with a technique that extract a set of components, which describes themaximum correlation between the response variable(s) and the predictors. The componentsare selected depending on how much variance they explain in the predictors, as well as thevariance between predictors and response variable(s) [44];

Two matrices, X(nˆp)

and Y(nˆq)

are assumed to be in linear decomposition like follow:

X = TP1 + E , where

$

’

&

’

%

T = x-scoreP1 = x-loadingE = x-residual

Y = UQ1 + F , where

$

’

&

’

%

U = y-scoreQ1 = y-loadingF = y-residual

PLS extract factors from X and Y such that covariance between the extracted factors is max-imized and x-, and y-scores are received. Each extracted x-, and y-score are linear combina-tions of X and Y respectively. Every x-, and y-score gets an eigenvalue and from these, U canbe estimated and thereby Y predicted. However, a function for the linear predictable modelcan be received based on the X and Y matrices.

The model is in the form of Y = XB + N , where

$

’

’

’

’

&

’

’

’

’

%

Y = response matrixX = predictors matrixB = regression coefficient matrixN = noise term

The different coefficients for all predictors are calculated from the linear regression line. Thevalue from the regression line is subtracted from the real value, the difference between thesetwo values are the error in the data. Hence, the coefficients are calculated as the square ofsums of error;

nř

i=1[yi ´ f (xi)]

2

20

3 Method and Materials

3.1 Method and materials

Data for parameters that were measured during the pilot study was available, see table 3.1.Additionally, new measurements were made for parameters that were measured during thepilot study as well as for new parameters. Samples were taken at different points in theozonation process and analyzed by different parties, the laboratory at TVAB, SYNLAB andAarhus University.

Table 3.1: Available data from measurements made and also measurements and on-line datafrom the pilot study.

New Data Historic Data (Laboratory) Historic Data (on-line)COD (dissolved + total) Nitrite TurbidityConductivity DOC TemperatureFe2+ Total Organic Carbon pHDOC Suspended solids FlowUVA UVA NitrateNitrite Gas flow (ozone injected)

Off-gas ozone (ozone residual)

3.1.1 Data analysis

The extensive on-line data of 43 557 data points for several parameters from the pilot studywas first structured based on days when the same concentration of ozone was used and alsodivided based on different gas flows. The on-line data was taken in intervals of six minutesand to reduce the amount of data as well as make it possible to include additional parame-ters, hourly and daily averages were calculated for the days of interest. The daily averagescalculated for the days of interest were used to investigate the influence of an altered gas flowon the ozone residual.

21

3.1. Method and materials

The ozone residual in the on-line data was recalculated from the unit g/Nm3 to mg/L usingthe gas flow and the flow of water as follows;

Ozone residual(mg/L) =Gas Flow(Nm3/h)ˆOzone residual(g/Nm3)

Flow(m3/h)

When the term ozone residual is used henceforth, it refers to the recalculated ozone residual.

Additional parameters were added to the on-line data, where simulated COD (COD(sim))was extracted from a Matlab model of Nykvarnsverket, Linköpingsmodellen and hence notincluded as a historical parameter. Also, nitrate was calculated based on a nitrite/nitratequotient. The quotient was obtained from analyzes of nitrate and nitrate concentrationsmeasured by the lab at TVAB for certain days during the pilot study. To obtain a quotient foreach day a linear approximation was made between measured data points where the dataof the first day of measurements to the second day of measurements gave the first linearapproximation, then the second and third day was used, and so on. The obtained quotientsfor each day of the pilot study were then multiplied with the nitrate concentration measuredon-line during the corresponding day, to obtain an accurate nitrite concentration. In furtheranalysis, this calculated nitrite concentration was used.

The final data used for the multivariate analysis included the parameters ozone dose, ozoneresidual, turbidity and nitrite from measurements during the pilot study as well as COD(sim)extracted from Linköpingsmodellen in hourly averages and was divided in two parts basedon different gas flows. The first part contained data from when different ozone doses weretested at a gas flow of 0.32 Nm3/h and the second part included days when some doseswere repeated as well as a regulatory strategy tested at a gas flow of 0.2 Nm3/h. Data wasalso prepared for a day that was deemed as ideal, where only the ozone dose and the nitriteconcentration were included. Moreover, data from a 15 day period during the summer of thepilot study was prepared which included ozone residual, nitrite, turbidity, COD(sim) andozone dose.

Results of pharmaceutical residue concentrations in samples taken before and after the ozona-tion during the pilot study were analyzed for diclofenac, metoprolol and oxazepam. The re-duction of pharmaceutical residues was calculated and compared for periods of the differentgas flows. These periods were divided further into three different trials, the dose-, control-and repeat trials. The dose trial was conducted with 12 different doses numbered 1-12 werethe ozone concentration ranged from 1.7 to 23.1 mg/L and the gas flow was 0.32 Nm3/h.During the control trials the offgas ozone was used in a regulatory strategy at two doses. Theozone residual was set at a desired limit based on the dose; 0.1 and 0.4 g O3/Nm3 for ozonedoses 5 and 7.5 mg/L, respectively and the ozone dose was regulated with a PI-loop basedon the ozone residual reference values. In the repeat trials measurements were made at dosescorresponding to the doses numbered 5 and 7 in the dose trials. The gas flow in both thecontrol and repeat trials was set at 0.2 Nm3/h. [8]

22


3.1.2 Multivariate analysis

The structured and sorted data was then ready for the multivariate analysis. The soft-ware used was Minitab 18 which is a statistical software and the license was received fromLinköping University. The multivariate analysis was performed with the purpose of obtain-ing a predictable model for the response variable, in this case ozone residual depending ondifferent predictors that may affected the ozone demand. In order to get the predictablemodel, a PLS regression method was used. A PLS is a standard method which was used tofind the correlation between different variables, that have different physical units.[45] Addi-tionally, a PCA was done before the PLS in order to exclude the outliers in the data. Minitabwas also used to fit data to a regression model.

PCA

In the first part, where different doses (dose trial) were tested, the data for ozone residual, ni-trite, turbidity, COD(sim) and ozone dose was exported from excel to Minitab. The functionprincipal components analysis was used and all the variables were selected for the analysis.Moreover, the type of matrix used was the Correlation matrix and the outlier plot was se-lected for visualization. The obvious outliers (i.e. the data point far over the reference line)were excluded from further analysis. However, many data points were on, or just over thereference line and to avoid lack of data, they were included in further analysis. This proce-dure was repeated for the second part (for the control-, and repeat trial data).

PLS

The PLS function in Minitab was used. As response, the ozone residual was chosen andnitrite, turbidity, COD(sim) and ozone dose were set to predictors (named as ‘Model’ inMinitab). Moreover, the coefficient for each predictor was selected to be stored in the resulttable. This procedure was repeated for the two different parts, the dose trial as well as thecontrol- and repeat trials.

Regression - fitted line plot

As mentioned above, data for a day that was deemed as ideal, with only ozone dose andnitrite concentration included was prepared. This data was used in order to get a fitted lineplot between these two variables. The function Regression - fitted line plot was selected andOzone dose was set as response variable (Y) and Nitrite was set as predictor (X). Moreover,the cubic form was set under the type of regression model. As a result, a predictable functionfor ozone dose was received.

3.1.3 Model validation and testing

The models from the multivariate analysis and the regression-fitted line plot were validated,the best model was selected for testing in MATLAB.

Model validation

Models from the multivariate analysis were validated using the data from a 15 day periodduring the summer of the pilot study that was prepared. The ozone residuals from the mul-tivariate models were compared with the ozone residual measured on-line during the 15 dayperiod of the pilot study. The model from the regression-fitted line plot was validated bycomparing the ozone dose from the model with ozone dose for days when the regulatorystrategy was used. The validation data and data from the models were compared for hourlyaverages and the best model was selected for further testing.

23


Model testing

The result from the multivariate analysis was a predictable model for the ozone residual.However, the aim was to predict the ozone dose during different conditions, which TVABcould include in their model, Linköpingsmodellen. The different ozone doses were calcu-lated in MATLAB.

MATLAB is a software as well as a programming language and is used for technical andmathematical calculations and simulations and the license for the software was receivedfrom Linköping University.[47] The best model chosen from the multivariate analysis wasincluded in the MATLAB-script. An initial dose for ozone was set to 6 mg/L and lower-,and higher limits for the calculated ozone were set to 4 and 8 mg/L, respectively in order toavoid the consequences for under-, and overdosing ozone. [9]

The script was divided in two steps in order to calculate the ozone dose, firstly the ozoneresidual was calculated based on the initial dose and a set of input parameters. Moreover, adose based on the old ozone residual (the ozone residual from previous step) and a new setof input parameters was calculated. These two steps were looped until the end of the test filein order to get a new doses for every set of input parameters.

3.1.4 Laboratory work

Measurements were made before the ozonation for standard sampling days, 2018-04-16, 2018-04-25 and 2018-05-02, at Nykvarnsverket. Additionally, measurements were made both be-fore and after the full-scale ozonation for three days, 2018-04-16, 2018-04-17 and 2018-04-19,for a variety of parameters.

Sampling days

Samples were collected hourly for 24 hours for three different sampling days, 2018-04-16,2018-04-25 and 2018-05-02. Permanent samplers in a sampling point after the biologicaltreatment and before the ozonation were used and the samples were analyzed for COD, Fe2+

and conductivity.

Measurements of the Fe2+-concentration before the ozonation were made using a spectropho-tometer at a wavelength of 510 nm in a 25 mL glass cuvette. 25 mL of water sampled beforethe ozonation was added to the cuvette and used as a blank. A HACH FerroVer powderpillow and 25 mL of sampled water was mixed in a beaker and then added to the glass cu-vette. The detection for total iron was in the range of 0.02-3 mg/L and measurements weremade using a program in the spectrophotometer. [48] Moreover, COD samples were sent toSYNLAB for measurements and conductivity was measured by the lab att TVAB.

24


Ozonation

The ozonation was started in Cactus, the guidance system used at Nykvarnsverket, wherethe ozone dose was set to a desired value. During the ozonation, samples were taken beforeand after ozonation using portable water samplers. The samples were collected in samplingtanks with a volume of 10 L using a peristaltic pump. 50 mL of water was sampled every 3minutes for 3 hours giving a total volume of 3 L for further analysis at both samplers. Thesampler before the ozonation was started at the same time as the ozonation whilst the samplerafter ozonation was started when the oxygen concentration in the ozonation showed a clearincreasing trend in Cactus, approximately after 15 minutes. Ozone doses of 5, 6.5 and 8 andmg/L were tested for 3 hours respectively at three different days 2018-04-16, 2018-04-17 and2018-04-19. [48] The samples taken at different sampling points were analyzed for a varietyof parameters, see table 3.2.

Table 3.2: Parameters that were measured before and after the ozonation step.

Before Ozonation After OzonationDOC NitriteNitrite UVASuspended Solids Pharmaceutical ResiduesUVACODPharmaceutical Residues

DOC, Nitrite and suspended solids were measured by the lab at TVAB. The methods usedwere SS-EN 1484-1 utg 1 for DOC, ISO 155923-1:2013 for nitrite and SS-EN 872:205 for sus-pended solids. [48]

UVA was measured using a UV-1700 PharmaSpec spectrometer, where the Photometric pro-gram was selected from the main menu and wavelength of 254 nm was set under Go to WL.MilliQ water was used as a blank and two measurements were made for samples from eachsample point and dose. A quarts flowing cuvette with a 1 cm light path was placed in thespectrometer and a sipper unit drew the sample into the cuvette. [48, 49]

COD samples were sent to SYNLAB for analysis and pharmaceutical residues were sent toAarhus University, for analysis. The pharmaceutical residue samples were shipped overnightin glass vials. For each sampling point and dose, five vials were prepared with 16 mL ofsampled water in each vial. The total volume for each sampling point and dose was 80 mL.[48]

25


3.1.5 Comparison between the pilot study and new measurements

The DOC, COD and Suspended solids data from new measurements were compared to datafrom the pilot study of the same time period (April). For COD the new measurements werecompared to COD(sim) data extracted from Linköpingsmodellen for corresponding days in2014. An average value was calculated for the three new measurements made at the dif-ferent ozone doses of 5, 6.5 and 8 mg/L. The difference between the average of the newmeasurements and the pilot study measurement was calculated. Then the change (increaseor decrease) was determined as followed;

Change (%) =Difference

Pilot study value=

New value´ Pilot study valuePilot study value

(3.1)

For UVA and nitrite, which were measured both before and after the ozonation, the differencein these values were calculated for each day and dose, respectively. UVA and nitrite reductionwere also calculated as follows;

Reduction (%) =Difference

Value before ozonation=

Before ozonation - After ozonationBefore ozonation

(3.2)

For UVA, the UVA reduction at 254 nm was then used to calculate the ozone sensitive UVAreduction. A constant of 56 %, which was determined during the pilot study as the amountof ozone sensitive UVA in the water was used. From the ozone sensitive UVA reduction theozone sensitive UVA left (%) was determined as follows;

Ozone sensitive UVAreduction(%) = UVAreduction ˆ 0.56

Ozone sensitive UVA left (%) = 1 - Ozone sensitive UVAreduction (3.3)

Data in ozone sensitive UVA left (%) for the different trials; dose-, control- and repeat trials,was compared with the newly calculated ozone sensitive UVA left (%). Also, the averagenitrite value before the ozonation was compared to an average from the 24 hour samplingday of the pilot study.

Moreover, on-line data measured during the pilot study was compared to on-line data mea-sured for the three different doses. Temperature, flow (of water), turbidity and nitrate at thedifferent doses were compared. Averages were calculated for the new data, first for the on-line data for the three different doses, respectively and then an average of the dose averages.The average values for all of the on-line data measured during the days of ozonation werethen compared to data from the pilot study. A difference between the new data and pilotstudy data as well as the change was calculated in the same way as for DOC and suspendedsolids.

Also, quotients were calculated between the measured nitrite and the on-line nitrate data atthe three doses. The average of this quotients was then compared to the average quotientfrom the pilot study data used in constructing and validating the models. Additionally, thechange in nitrite and nitrate between new measurements and the pilot study were compared.

Results on the pharmaceutical residue concentrations before and after the ozonation, ob-tained from Aarhus University, were compared with measurements made during the pilotstudy and the reductions were calculated with equation 3.2. Conductivity and Fe2+ were notmeasured during the pilot study and no comparison was made.

26

4 Results and Discussion

4.1 Verification of the pilot study

The pilot study was verified by comparing various parameters measured during the pilotstudy with new measurements made for these parameters. Additionally, the reduction ofpharmaceutical residues was verified by comparing the reduction from the newly made mea-surements with the pilot study.

4.1.1 Parameters

During the new measurements on the full-scale facility, on-line data of temperature, flow, tur-bidity and nitrate were measured. Additionally, measurements were made for DOC, COD,suspended solids, nitrite and UVA. The results from these measurements were comparedwith historical data from the pilot study.

On-line data for the new measurements, see table 4.1, was compared with on-line data fromthe pilot study. Temperature, flow and turbidity all had minor changes from the pilot studywhilst nitrate increased significantly with 87 %. The increased nitrate concentration might bea result of a low removal in the biological treatment step prior to the ozonation. Both flow andtemperature are known to influence this step, where a lower temperature results in a lowernitrogen removal and a higher flow shortens the retention time in the biological treatmentstep. A shorter retention time leads to there being less time available for denitrification andintermediate species in the nitrogen removal, nitrate and nitrite, are therefore found at higherconcentrations after the biological treatment. Although, there are minor differences betweenthe pilot study and the new measurements for these parameters. Therefore, even if flow andtemperature affect the nitrate concentration to a small extent, the significant increase resultsfrom the impact of another factor. During the measurements on the full-scale facility, buildingwork at Nykvarnsverket caused the ammonium concentration to be 15-20 % higher than whatis normal. The significant increase in nitrate concentration is hence a result of a combinationof the changes in flow and temperature as well as the effects from the building work. [13, 17,40]

27

4.1. Verification of the pilot study

Table 4.1: Daily averages of on-line data for the pilot study and the new measurements onthe ozone doses of 5, 6.5 and 8 mg/L. The change was calculated according to equation 3.1.

Parameter Pilot study Dose 5 Dose 6.5 Dose 8 Average ChangeTemperature (˝C) 14.13 13.1431 14.40042 13.62083 13.72145 -3%Flow (m3/h) 1751.417 1848.084 1791.525 1861.879 1833.829 5%Turbidity (FNU) 5.84583 3.700418 8.50125 5.511667 5.904445 1%Nitrate (mg/L) 7.515278 13.25983 13.79 15.14292 14.06425 87%

COD was not measured during the pilot study, but introduced as a parameter of interestduring this thesis. As no measurement data was available for daily variations of COD fromthe pilot study, values were extracted from Linköpingsmodellen, a MATLAB model of Nyk-varnsverket. The COD(sim) data extracted from Linköpingsmodellen is given from midnightto midnight with 15 minute intervals between each data point. In the model, the COD(sim)concentration is inert whilst the flow varies, hence the variations observed in COD(sim)are the result of dilution at higher flows. This means that the COD(sim) is higher duringthe summer, when the load is lower at Nykvarnsverket. However, this is not the expectedbehavior, a decrease in load is expected to also result in a lower COD. Hence, the results fromthe Linköpingsmodellen are somewhat inaccurate. [50]

The daily variations in COD(sim) from Linköpingsmodellen had the same behavior as otherparameters measured hourly during a 24 h sampling day of the pilot study, DOC and UVA.Also, a delay between the flow minimum and the minimum of parameters was observed, seefigure 4.1. Additionally, nitrite had an earlier minimum and followed the flow more thanthe other parameters. The daily variations of these parameters could not be measured duringthis thesis due to weather conditions. Instead daily averages of DOC, COD(sim), UVA, nitriteand flow were compared.

Figure 4.1: Daily variations of COD(sim) extracted from Linköpingsmodellen and measuredvariations in DOC, UVA, nitrite and flow during the pilot study.

28


The new measurements of DOC, COD and Suspended solids, were only made before theozonation. DOC, COD and suspended solids were measured for each of the three ozonedoses, 5, 6.5 and 8 mg/L, and average values were calculated, see table 4.2.

DOC from the pilot study is the average value of the daily variation data, see figure 4.1.The average value from the three new measurements is higher than the value of the pilotstudy which means that a higher ozone dose is required in the full-scale ozonation. The DOCconcentration has increased with 48% since the pilot study measurements were made fouryear ago. Suspended solids, meaning solid particles suspended in the wastewater, has unlikeDOC decreased with 60% since the pilot study.

COD results were only obtained for one measurement at the ozone dose of 8 mg/L due tomiscommunications with SYNLAB. Therefore, a concentration of 44 mg/L was assumed forall three doses. The COD concentration during the new measurements is higher than theaverage value for COD(sim) calculated for the corresponding days in 2014 and COD hasincreased with 36 %. As the COD(sim) value probably is not entirely correct, being based aninert concentration that varies with the flow, part of the difference is likely due to this. [13,18, 19]

Table 4.2: Parameters that influence the ozonation measured during the pilot study as well asnew measurements made on the ozone doses of 5, 6.5 and 8 mg/L. The change was calculatedaccording to equation 3.1.

Parameter Pilot study Dose 5 Dose 6.5 Dose 8 Average ChangeDOC (mg/L) 9.94 13 15 16 14.7 48%COD (mg/L) 32.4 (sim) 44 44 44 44 36%Suspended solids (mg/L) 12 4 4.7 5.7 4.8 -60%

UVA and nitrite were measured both before and after the ozonation and the reductionswere calculated and compared to data from the pilot study. In the nitrite concentrations formeasurements made on the full scale ozonation, see table 4.3, no clear trend can be observed.However, the nitrite reduction is high for all ozone doses and even above 90% at the highestdose of 8 mg/L.

During the pilot study the nitrite concentration was only measured before the ozonation.Therefore, the nitrite concentration before the ozonation for the average value of the dailyvariation data of 0.34 mg/L, see figure 4.1, was compared to the average for the new mea-surements of 0.43 mg/L. This is an increase of 0.09 mg/L, or 28 %, in the nitrite concentrationinto the ozonation. As nitrite is oxidized to nitrate by ozone with a molar ratio of 1:1 theincrease in nitrite concentration corresponds to an increase in ozone demand. It is the NO2-Nthat is of interest when calculating the change in ozone demand as follows;

n(NO2 ´ N)/L =m(nitrit)

M(nitrogen)=

0.09mg/L14g/mole

= 0.0064286mmole/L

Molar ratio 1:1 gives 0.0064286 mmole/L ozone

m(O3) = n(O3)ˆM(O3) = 0.0064286mmole/Lˆ 48g/mole = 0.3086mg/L

Hence the increase in nitrite concentration of 0.09 mg/L gives an increase in ozone demandof 0.31 mg/L. [8]

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Table 4.3: The nitrite concentration before and after ozonation at the tested ozone doses. Thereduction was calculated according to equation 3.2.

Ozone dose(mg/L)

Before ozonation (mg/L) After ozonation (mg/L) Difference Nitritereduction

5 0.35 0.064 0.286 82%6.5 0.52 0.11 0.41 79%8 0.42 0.035 0.385 92%Average 0.43 0.069667 0.36033 84%

UVA results from the full-scale measurements at 254 nm, see table 4.4, before and after ozona-tion were used to calculate UVA reductions. As the substances that can be reduced by ozonedoes not account for the entire UVA it is necessary to determine the UVA reduction for theozone sensitive substances. The percentage of ozone sensitive UVA was determined to be56% during the pilot study and this value was used in calculations with new data as well.

Table 4.4: UVA results from measurements before and after the ozonation on the full-scaleozonation.The ozone sensitive UVA left was calculated according to equation 3.3.

Ozone Dose(mg/L)

Before Ozonation After Ozonation Ozone sensitiveUVA reduction

Ozone sensitiveUVA left

5 0.252 0.185 47% 53%6.5 0.302 0.222 47% 53%8 0.279 0.1825 61% 39%

The ozone sensitive UVA left from new measurements was compared to data from the fromthe pilot study for the three different trials; the dose-, control- and repeat trials. Also, theUVA results were connected to known ozone consuming parameters that have similar dailyvariations, DOC and nitrite, see figure 4.1. The nitrite concentration was calculated basedon an average nitrite/nitrate quotient of 0.04 for the data from the dose, control and repeattrials as well as data from a 15 day period during the summer from the pilot study. whichwas multiplied with on-line nitrate concentrations. When looking at only the ozone dose,see figure 4.2, it appears that there is a higher amount of ozone sensitive UVA left for thefull-scale measurements than the trials during the pilot study. However, the dose trial hasthe lowest levels of ozone sensitive UVA left of the trials which is most likely due to the factthat the dose trial was conducted during a lower load period.

The amount of ozone sensitive UVA left, when connecting the ozone dose and UVA resultsto DOC, is significantly higher for the full-scale measurements compared to the dose trial. Itcan also be seen that both the control- and repeat trials have a higher level of ozone sensitiveUVA left than the dose trial. The difference between the trials is likely due to the fact that theload was lower during the dose trial. However, a constant DOC concentration of 10 mg/Lwas assumed throughout the pilot study and multiple parameters influence the ozonationas well as the UVA reduction. Therefore, seeing a direct correlation to one ozone consumingparameter is difficult, especially when it is at a constant value since variations in load are notincluded if that is the case.

For nitrite, no clear difference can be seen for the different trials. However, the new mea-surements still show a higher amount of ozone sensitive UVA left compared to the dose trial,which can be connected to the increase in nitrite in the new measurements compared to thepilot study. Moreover, unlike DOC, the nitrite concentration was calculated from the on-linemeasurements of nitrate, where the concentration varied with the load.

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Figure 4.2: The ozone sensitive UVA left against A) The ozone dose, B) The ozone dosedivided by DOC concentration C) The ozone dose divided by nitrite concentrations.

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The differences observed for the various parameters between the pilot study and the newmeasurements are thought to come from a variety of factors. The growth in population, anincreased load and the weather conditions are all expected to have affected the wastewatertreatment process. However, while some parameters clearly have increased, like nitrate andDOC, others for example suspended solids have decreased and some parameters, like tem-perature and turbidity, have remained at approximately the same level. Hence, it cannot besaid that all factors have resulted in an increase in all parameters and therefore a higher ozonedose demand.

4.1.2 Gas flow

Two different gas flows were tested during the pilot study of 0.32 to 0.2 Nm3/h for thedifferent trials. The dose trial had a gas flow of 0.32 Nm3/h whilst both the control-, andrepeat trials had a gas flow of 0.2 Nm3/h. Comparing these trials, see figure 4.3, shows thatthe dose trial has a higher residual for the same dose than the control-, and repeat trials.Moreover, the control and repeat trials have very similar values for the ozone residual.

The cause of the difference between the dose trial and the other trials is thought to be analtered gas flow (the flow rate of gas being mixed with the wastewater in Nm3/h) from 0.32to 0.2 Nm3/h. Hence, the gas flow has an effect on the ozone residual and is the cause ofthe variation between the trials. An increased gas flow rate reduces the MTE when the gasis mixed with the water which results in a higher ozone residual, as the ozone cannot beconsumed unless it is transferred into the aquatic phase. [13] Moreover, the influence ofother parameter such as nitrite or DOC might also have affected the ozone residual. Alsoworth noting is that the gas flow out of the ozonation was not measured, but assumed to beequal to the gas flow of ozone in to the ozonation. Hence, this could have affected the ozoneresidual measurements.

Figure 4.3: The ozone residual against the ozone dose for the dose-, control-, and repeattrials with exponential trend lines.

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4.1.3 Pharmaceutical residues

The pharmaceutical residues of interest diclofenac, metoprolol and oxazepam were reducedduring the pilot study in the ozone doses within the interval 4-8 mg/L. The optimal ozonedose required to achieve a 90 % reduction of pharmaceuticals is expected to be in this interval.Diclofenac was completely reduced at doses lower than this interval whilst oxazepam wasmost resistant to ozonation of the pharmaceuticals tested during the pilot study and was notsufficiently reduced in the optimal interval. However, oxazepam was detected at a higherlevel than expected in this interval as a result of variations as measurements were taken be-fore and after the summer holiday, a 90 % reduction is therefore thought to be possible in theoptimal interval. Also, measurements made every hour for 24 hours of the pharmaceuticalsof interest during the pilot study shows that the concentration of diclofenac, metoprolol andoxazepam decrease in the morning and have minimum concentrations observed around thesame time as for other parameters, DOC, COD, UVA and nitrite. [8, 50]

A difference in reduction of pharmaceutical residues was observed when comparing thethree different trials of the pilot study; the dose-, control- and repeat trials. Diclofenac was100 % reduced at doses lower than those tested during the control- and repeat trials, thereforeno difference in reduction was observed between the trials. However, results from the dosetrials show a higher reduction than for the other trials for metoprolol and oxazepam, seefigure 4.4. The difference between the trials might be affected by an altered gas flow (the flowrate of gas being mixed with the wastewater in Nm3/h) from 0.32 to 0.2 Nm3/h. A lowergas flow, which gives a better MTE as well as a higher percentage of ozone in the gas mixedwith the water, should give an improved reduction [13]. However, this was not the caseduring the pilot study, which gives an indication to the complexity of the ozonation process.Considering only the gas flow a different result was expected, but due to the influence ofother parameters this was not observed. Hence, the influence of multiple parameters andthe relationship between parameters must be studied to understand differences observedduring ozonation. [13, 17] With a higher nitrite concentration during the control- and repeattrials than the dose trial, the ozone dose required to achieve an equivalent reduction to thatof the dose trial was higher. Moreover, the load (flow of water into Nykvarnsverket) washigher during the control- and repeat trials which is also likely to mean a higher ozone doserequirement to achieve the same reduction of pharmaceutical residues between the trials.[17, 50]

The new measurements made on samples from the full-scale facility of pharmaceuticalresidues, see table 4.5, show that the highest reduction was obtained for the highest ozonedose of 8 mg/L during the new measurements. For diclofenac, the reduction is slightlyhigher at the dose of 6.5 mg/L which is unexpected as a higher ozone dose should resultin a higher reduction of pharmaceutical residues [8, 13, 18]. However, when comparing thechanges for parameters measured before the ozonation, see table 4.2, the DOC concentrationwas lower at the lowest ozone dose of 5 mg/L. Also, the turbidity was slightly higher at the6.5 mg/L ozone dose. Hence, the impact of different parameters have resulted in a the re-duction of diclofenac being lower than expected. Metoprolol, on the other hand show a cleartrend where a higher ozone dose corresponds to a higher reduction. Although, the param-eters which affected the diclofenac reduction at an ozone dose of 6.5 mg/L have likely alsoimpacted the metoprolol reduction. For oxazepam no reduction could be measured with theanalysis method used at Aarhus University.

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Table 4.5: Reduction of diclofenac and metoprolol during the new measurements on ozonedoses of 5, 6.5 and 8 mg/L. Sadly, oxazepam could not be measured with the method used.The reduction was calculated accoring to equation 3.2.

Pharmaceuticalresidue

Dose(mg/L)

Before ozonation (µg/L) After ozonation (µg/L) Reduction

Diclofenac 5 1.09 0.16 85.3211%6.5 0.94 0.16 82.9787%8 0.98 0.06 93.8776%

Metoprolol 5 1.66 0.81 51.2048%6.5 1.82 0.8 56.1264 %8 1.72 0.47 72.5656 %

When comparing the reduction of pharmaceutical residues during the pilot study in 2014and during new measurements made on the full-scale facility, see figure 4.4, in the optimalinterval (4-8 mg/L) diclofenac and metoprolol both had a lower reduction than what wasobserved during the dose trials of the pilot study. For diclofenac, the reduction was 100 %in the optimal interval during the pilot study, whilst for the new measurements the reduc-tion is only above 90 % at the highest ozone dose of 8 mg/L. Metoprolol also has a lowerreduction during the new measurements compared to the dose trial. However, comparedto the control-, and repeat trial the metoprolol reduction during the new measurements ismarginally higher. Also, for metoprolol a slightly higher reduction would be expected at theozone dose of 6.5 mg/L in order for the same behavior as the dose trial to be observed, seefigure 4.4. Hence, the parameters which impacted the reduction of diclofenac at the 6.5 mg/Lozone dose also affect the metoprolol reduction.

The difference observed between the new measurements and the pilot study are expectedto be a result from the impact of multiple parameters and factors. One factor is that thereare four years between the measurements and during that time the town of Linköping hasgrown. Also, there are differences between the pilot plant and the full-scale facility, the waterflow and the gas flow both varied during the full-scale measurements, while having beenkept at constant levels during the pilot study. Moreover, the basin in the full-scale facility hasa different configuration, volume and method for mixing the ozone with the wastewater thanthe pilot plant, which can have an effect on the reduction of pharmaceutical residues. Addi-tionally, measurements were made during high load whilst the dose trial results from thepilot study are from the low load summer period. Also, the increased population of approx-imately 7000 inhabitants, means an increase in pharmaceutical residues and together withthe fact that people use more pharmaceuticals this means a higher load of pharmaceuticalresidues in the influent to Nylvarnsverket. Worth noting is that the analysis of pharmaceu-tical residues were performed by different laboratories and therefore a comparison betweenincoming concentrations of pharmaceutical residues to the ozonation cannot be made. [1, 8,42]

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Figure 4.4: The reduction of A) Diclofenac, B) Metoprolol and C) Oxazepam during thethree trials; dose-, control- and repeat trials of the pilot study and the reduction from mea-surements made on the full-scale facility. Sadly, oxazepam could not be measured with themethod used.

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A linear relationship between the ozone sensitive UVA left and an average of the amount ofdiclofenac, metoprolol and oxazepam remaining after ozonation can be observed for the dosetrial, see figure 4.5. The control- and repeat trials also fall in close proximity of this linearity,apart from one outlier. The outlier, marked with black arrows correspond to a day during thedose trial and is positioned quite far from the line for the linear relationship. As expected,when looking at data for this outlier no lone parameters can be said to be the underlyingcause of the deviation from the linear relationship for this point. As the ozonation is influ-enced by multiple parameters and the relationship between them the cause of the deviationcannot be determined. [13, 18, 19]

However, the linear relationship does give a promising starting point for a control strategyusing UVA. The goal of a 90 % reduction of pharmaceutical residues, which is required toeliminate the environmental effects, is achieved at 20 % or less of ozone sensitive UVA left,apart from the outlier, for diclofenac, metoprolol and oxazepam. Worth noting is that ox-azepam, which lack ozone reactive groups and was shown to be most persistant to ozonationduring the pilot study, might require a lower level of ozone sensitive UVA left in order toachieve a 90 % reduction.

For the new measurements an average was calculated for the reduction of diclofenac andmetoprolol. This average, in pharmaceutical residues remaining after ozonation, was thencompared with the ozone sensitive UVA left from the new measurements. The results fromthe new measurements fall in close proximity of the linear trend line observed for the dosetrial of the pilot study. This further emphasises that UVA is promising to use in a controlstrategy and also that the linear relationship from the dose trial can be applied to the full-scale process as well. However, it is worth noting that only diclofenac and metoprolol wereincluded in the new measurements and that adding oxazepam might have impacted the re-sults as this influenced the average calculated for the pilot study..

Figure 4.5: The average for diclofenac, metoprolol and oxazepam remaining after ozonationagainst the ozone sensitive UVA left for the different trials and the new measurements aswell as a linear trendline for the dose trial. The black arrow mark the outlier.

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The reduction of pharmaceutical residues for the new measurements is similar to the reduc-tions measured during the pilot study. Especially the relationship between pharmaceuticalresidues remaining after ozonation and ozone sensitive UVA left shows that the pilot studyand the full-scale facility give similar results. Even though several parameters, for exam-ple DOC, nitrate and suspended solids have been altered significantly, the pharmaceuticalresidue reduction is still similar. Also, the effects of factor such as the growth in populationin Linköping and the difference in design between the pilot plant and the full-scale facilitydoes not lead to a large difference in the reduction of pharmaceutical residues. Hence, theresults from the new measurements on the full-scale facility validate the pilot study resultsbased on the reduction of pharmaceutical residues.

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4.2. Model construction

4.2 Model construction

Three different models were constructed based on different data from the pilot study. Thesemodels were then validated with pilot study data. The best model was selected and usedas the basis for a MATLAB simulation, wherein the ozone dose at different conditions ispredicted. The MATLAB simulation was tested with data both from the pilot study and thenew measurements.

4.2.1 Multivariate analysis and regression model

Three different models were constructed based on data from the pilot study, two modelsbased on multivariate analysis (MVA1-model and MVA2-model) and a nitrite model (N-model). The two multivariate models were based on data from the different gas flows of0.2 and 0.32 Nm3/h for the MVA1-model and the MVA2-model, respectively. Parametersthat are known to influence the ozone requirement and with abundant data available wereincluded in the multivariate analysis.

PCA was conducted before the PLS and the regression analysis to exclude outliers in the data.As the models were to be validates with data from the pilot study, no standardization wasperformed before the analysis. In a PLS the final model is given in both the original and astandardized form. However, the original form was used when validating and testing themodels.

MVA-models

In the first PLS (MVA1), hourly average data for different doses (from the dose trial) wasanalyzed. The results have been compiled in a table, see table 4.6. The F-value as well asp-value determined if the model was associated to the response and statistically significant.A high F-value and thereby a low p-value (241,01 and 0,000 respectively) indicated a signifi-cant model. Moreover, the R-sq value, i.e. the value between 0%-100% (0% = a bad fit, 100%=optimal fit) that determined how well the model fits the data, was quite high for the model.With all four components, the total R-sq for the model was approximately 80%. Additionally,the amount of variance that is explained by the model i.e. the X-variance (a value between0.0 and 1.0). The X-variance for all components was 1.0, which means that the componentsrepresent the original set of terms. [51]

In the second PLS (MVA2), hourly average data for the control-, and repeat trial were ana-lyzed. The results have been compiled in table 4.6. This model had a F-value of 132,50 andp-value of 0,00 indicating a significant model. The R-sq value was in total (with all compo-nents) approximately 73%, indicating a good fit to the model. However, it is a lower valuethan for the MVA1 model. Additionally, the X-variance reach 1.0 with all four components incount. [51] This model seemed significant and a potential model for further analysis.

Table 4.6: Analysis of Variance and Model-, and Validation selection for MVA1 and MVA2.

F-value p-value R-sq X-varianceMVA1 241.01 0.000 0.794729 1.0MVA2 132.50 0.000 0.725038 1.0

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A MVA1-model and a MVA2-model were obtained from the first and second PLS, respec-tively. Coefficients were received for the parameters included in the PLS, see table 4.7.

Table 4.7: The coefficients received from MVA1 and MVA2 for the analyzed parameters.

Parameter Variable Coefficient (MVA1) Coefficient (MVA2)Constant k -0.15854 -0.0362444Nitrite X1 -0.73273 -0.0616523Turbidity X2 -0.00365 -0.0004939COD(sim) X3 0.002275 0.0004026Ozone dose X4 0.07299 0.0130178

These coefficients were used in equations to predict the ozone residual (Y), which results in aMVA1-model and MVA2-model as follows;

Y = ´0, 15854´ 0.73273ˆ X1 ´ 0.00365ˆ X2 + 0.002275ˆ X3 + 0.07299ˆ X4

Y = ´0.0362444´ 0.0616523ˆ X1 ´ 0.0004939ˆ X2 + 0.0004026ˆ X3 + 0.0130178ˆ X4

Nitrite and turbidity had negative constants in both MVA-models which was expected. Ahigher value of these two predictors, gives a lower ozone residual which means a higherozone demand. These results conform with other studies [13, 18, 19, 50]. The ozone dosehad a positive constant in both models, which also was expected. A higher dose of ozonewould result in a higher ozone residual, i.e. it was more ozone left when the ozone dose washigh. On the other hand, the COD had a positive constant as well, which was unexpected.Studies have shown that COD affect the ozone demand, meaning that if COD-value is high,more ozone is required. Worth noting is that the data for COD in this case is simulated andreal values for COD at Nykvarnsverket were not available in the historic data from the pilotstudy. The simulated data has a much smoother appearance than measured data, whichresults in lower variations in the multivariate analysis. This might be the explanation forthe positive COD(sim) constants in these models. However, since it is shown that COD is aparameter which affect the ozone demand, the decision was taken that this parameter shouldbe included in the model. [18, 19, 50]. Comparing the coefficients in these MVA-models showthat all coefficients are lower in the MVA2-model, which means that this model will predict alower ozone residual than the MVA1-model for the same input data.

Nitrite model

The N-model was based on data from an ideal day, when the behavior of nitrite and the ozoneresidual was as expected, meaning they followed the same trend, see figure 4.6. The ozonedose (y) was set against the nitrite concentration (x) in a Regression - fitted line plot, whichrendered the equation;

y = ´17.34 + 109.1x´ 176.4x2 + 98.4x3

The R-sq of 83.20% is high, indicating a good fit to the model. However, it is only possible touse this model for days when the control strategy was used as the ozone dose was allowedto vary during these days, instead of having a set value. Comparing the equation valueswith the measured values of the ozone dose of the ideal day as well as other days when thecontrol strategy was deployed, see figure 4.6, shows that the model initially fits the data well.However, this is only true for the data that was used to construct the model. Thereafter themodel differs from the measured data and as the set value for the dose in the control strategyincreases from 5 to 7.5 mg/L, at date 2014-10-26, the model cannot be said to predict the ozonedose accurately based on the nitrate concentration.

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Although, this model cannot be entirely discarded since the nitrate concentration was onlymeasured sporadically during the pilot study. To obtain a nitrite concentration a quotientwas used which might not have been entirely accurate for some days. Also, the nitrite quo-tient was only available per day and then used to determine the hourly nitrite concentrationswhich might also contribute to the difference between the model and the measured data.Moreover, as ozonation is a very complex process, which is influenced by multiple parame-ters, using only nitrite to predict the ozone dose can give faulty results due to the influenceof other parameters, like COD or turbidity.

Figure 4.6: A) The measured ozone dose and the nitrite concentration over the day that theN-model was based on. B) The measured ozone dose compared to the dose predicted by theN-model during the days when the regulatory strategy was deployed.

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4.2.2 Model selection and validation

Comparing the coefficients in the MVA-models show that the ozone dose has a higher impacton the ozone residual in the MVA1-model. Also, nitrite has a much higher constant, whichmeans a greater influence on the ozone residual in the MVA1-model. Both MVA-modelshave constants which are negative, as expected, for nitrite and turbidity as well as a positiveconstant for the ozone dose. However, the positive COD(sim) constant disagrees with theliterature. [18, 19] Although, the explanation of the positive constant is thought to come fromthe way COD is calculated in Linköpingsmodellen where the COD concentration is inertwhilst the flow varies, hence the variations observed in COD(sim) are the result of dilutionat higher flows. Additionally, the lower influence of the ozone dose in the MVA2-modelis likely due to the fact that the ozone dose in the data used to determine this model onlyranged between 5-8 mg/L. This means that the variation in ozone dose was lower and henceless variation means less influence, represented by the lower coefficient value.

Data from the pilot study was used to determine which model was the best fit to experi-mental data, thereby verifying the accuracy of the models in predicting the ozone demandat different conditions. A time period of 15 days during the summer, where the ozonedose was kept at a dose of 9.75˘0.3 mg/L was selected and measurement points with anabnormally high ozone residual were removed. The ozone residual was calculated using theMVA1-model and MVA2-model, respectively and compared to the measured ozone residual,see figure 4.7. The MVA1-model follows the data better, whilst the MVA2-model has a muchlower residual then measured. This shows that the MVA1-model is better in predicting theozone residual based on some of the parameters known to influence the ozone demand.Also, the coefficients in the MVA2-model are lower in general and are deemed as unreliabledue to the bad correlation between the ozone residual predicted by the MVA2-model andthe measured values. Worth noting is that the MVA2-model was based on data with a gasflow of 0.2 Nm3/h whilst the test data from the pilot study had the 0.32 Nm3/h gas flow.However, there is a very large error for the MVA2-model compared to the data, which is notcaused solely by the gas flow but is also due to the data used in determining the equations.Moreover, both models have the same behavior in where they increase and decrease but theMVA2-model has much lower values than the measured ozone residual. As a result of thisthe MVA1-model is said to be valid in predicting the ozone residual whilst the MVA2-modelis discarded.

Even though the MVA1-model fits the validation data well, the ozone residual predictedby the model lies slightly higher than the pilot data. This indicates that the model is notperfect, which is expected as more parameters that are not included in the model influencethe ozonation. For example, DOC, where there is a lack of data and flow, which has a delaycompared to other parameters, were not possible to include in the model.

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Figure 4.7: The measured ozone residual compared to the residual predicted by the MVA1-model and MVA2-model for hourly averages of 15 days during the summer, where theozone dose was kept at 9.75˘0.3 mg/L.

When comparing the results obtained from the model with the data used to create the model,see figure 4.8, there is a good fit. The ozone residual from the MVA1-model follow the mea-sured data well. Negative values for the ozone residual were obtained at the lowest ozonedoses of 1.7- 3.7 mg/L in the data used for the model. These negative values are not expectedto generate issues when ozone doses are applied in the interval of 4-8 mg/L. A negative ozoneresidual is only possible in theory, although this shows that the lower ozone doses cannot notmeet the ozone demand and therefore a higher ozone dose is required.

Figure 4.8: The measured ozone residual compared to MVA1-model residual for the dataused to construct the MVA1-model.

Nitrite is difficult to measure on-line and therefore nitrate is measured instead. [17] A quo-tient between nitrite/nitrate can be used to determine the nitrite concentration required inthe model. In this case different quotients were used for different days to ensure that asmuch variation as possible was included in the multivariate analysis. However, an averagequotient of 0.04 was calculated for the data used in constructing and validating the MVA1-model. It is worth noting that this quotient is based only on data from the pilot study andmight have changed since 2014.

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From the nitrite concentrations measured before the ozonation on the full-scale facility, seetable 4.3, and the on-line nitrate measurements a quotient was calculated, see table 4.8. Thisnew quotient has an average of 0.031, which is lower than the average quotient of the pilotstudy. Hence, the ratio of nitrate to nitrite has been altered since the pilot study. The nitrateconcentration has increased with 87% and the nitrite concentration has only increased with28% since the pilot study. Therefore, the ratio of nitrite to nitrate is lower and thereby also thenitrite/nitrate quotient. Moreover, measurements on the full-scale facility only were madefor a three hour period during the day, with a lower rate of nitrification than during the pilotstudy and with continuous aeration. This will also affect the measured nitrite concentrationsand be part of the reason for the altered quotient. However, additional measurements wouldgive a more exact value for the nitrite/nitrate quotient.

Table 4.8: Nitrite and nitrate concentrations before ozonation for the different doses as wellas calculated quotients and averages.

Dose (mg/L) Nitrite concentration (mg/L) Nitrate concentration (mg/L) Quotient5.0 0.35 13.25983 0.0263966.5 0.42 15.14292 0.0277368 0.52 13.79 0.037708Average 0.43 14.06425 0.030613

Also, COD measurements are not made at TVAB and this parameter is not included whenanalyzes are performed after sampling days. Therefore, DOC would be of interest to includein a model instead as measurements are made at TVAB for this parameter. However, aslimited data was available for DOC during the pilot study, COD(sim) was used in the modelsinstead. Using a constant for the relationship between COD(sim) and DOC of 3.73 in theMVA1-model together with the COD coefficient of 0.002275 and measured DOC data givesan almost identical behaviour to the original MVA1-model, when looking at the days used forvalidation. For the new measurements this constant between COD and DOC is at 3, whichis slightly lower than for the pilot study. This is due to a larger increase in DOC than COD,although more data is required for an accurate determination of this constant.

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4.2.3 Model testing

The MVA1-model was used as the basis for a model constructed in MATLAB that predict theozone dose at a set of conditions. This model was then tested using the same data as for thevalidation above as well as with data from the full-scale measurements.

The procedure

From the MVA1-model the ozone residual is calculated for different conditions. However, it isthe ozone dose required at a given set of conditions that is of interest. Therefore a simulationin MATLAB was constructed based on the MVA1-model. The output from the simulationwas a prediction of the ozone dose (Ozone Dose(sim)). A test file with the same data as forthe validation (2014-07-19 to 2014-08-02) for nitrite, turbidity and COD(sim) was made. Theprocedure for Ozone Dose(sim) is as follows;

1. In the first iteration, an initial dose (Xi) of 6 mg/L was set. Moreover, the first set ofparameters (i) in the test file were read and the different values for nitrite, turbidityand COD(sim) in addition to the initial dose were set to input parameters. An ozoneresidual (Yi) was calculated and stored for the second iteration, see eq. 4.1.

2. The next set of parameters (i+1) in test file were read together with the dose from theprevious step (dose from step 1 in the first iteration, dose from step 3 for every other it-eration) and used to calculate a a new ozone residual (yi+1) which was stored, accordingto eq. 4.1.

3. With the values from the same set of parameters as in step 2 (i+1), in addition to ozoneresidual from the previous iteration (Yi) a new dose was calculated (Xi+1), according toeq. 4.2.

The steps 2 and 3 were looped until the end of the test file and i=0,1,2,....end of test file (last setof parameters). The output was a list of the different ozone doses. For each set of parametersin the test file, the dose was calculated based on the current values for nitrite, turbidity andCOD(sim) in addition to the previous value for the ozone residual. For an overview of themodel testing, see figure 4.9, moreover a part of the script is available in Appendix A.1

Yresidual = ´0, 15854´ 0.73273ˆ X1 ´ 0.00365ˆ X2 + 0.002275ˆ X3 + 0.07299ˆ X4 (4.1)

Xdose =Yi´1 + 0, 15854 + 0.73273ˆ X1 + 0.00365ˆ X2 ´ 0.002275ˆ X3

0.07299(4.2)

44


Figure 4.9: An overview of the Ozone Dose(sim) model

Ozone Dose(sim) validation

Comparing the ozone dose to the other input parameters, see figure 4.10, for two days ofthe 15 days used in the validation and testing shows that the ozone dose and the inputparameters have the same trends. The reason for not including all 15 days when comparing,was the unclear graphs that appeared. However, trends can be observed for all days, but thetrends were clearer when looking at two days. Days chosen were in the beginning and in themiddle of the period.

For nitrite and turbidity the expected behavior was observed as the constants in the MVA1-model are negative for these parameters. However, COD has a positive constant and shouldhence increase when the ozone dose decreases according to the MVA1-model. Theoretically,and according to previous studies [18, 19, 50], an increase in COD corresponds to a decrease inthe ozone residual and thereby an increased ozone demand. This means that the ozone doseshould be increased as the COD increases to ensure that the required ozone dose is applied.According to the Ozone Dose(sim)-model the expected behavior from theory is observed, inspite of the positive MVA1-constant for COD(sim). This is thought to be a result of COD(sim)data having been used in the PLS which gave the MVA1-model.

45


Figure 4.10: The ozone dose from the Ozone Dose(sim)-model and measured A) Nitrite, B)Turbidity and C) COD(sim) variations for two days; 2014-07-19 and 2014-07-27, during the15 day validation period used.

46


Verification

The Ozone Dose(sim)-model was tested with full-scale data for nitrite, turbidity and COD,see figure 4.11. For the first input data the initial ozone dose predicted by the model duringthe first iteration is always 6 mg/L. This is due to the fact that the initial ozone dose, for thefirst set of input parameters, is set to 6 mg/L in the Ozone Dose(sim)-model. For the secondset of input parameters the ozone dose is predicted to be approximately 7.9 mg/L whilst thethird set of input parameters give an ozone dose of 4.8 mg/L. Of the parameters includedin the model, COD was at a constant level of 44 mg/L due to the lack of data resulting frommiscommunications and therefore not included in figure 4.11. However, higher values forboth nitrite and turbidity leads to a higher predicted ozone dose by the Ozone Dose(sim) asexpected from literature. [13, 17] Moreover, the predicted ozone doses for the data from thefull-scale facility are within the optimal interval for the ozone dose of 4-8 mg/L.

Figure 4.11: The ozone dose predicted by the Ozone Dose(sim) (in mg/L on primary y-axis)the turbidity (in FNU on primary y-axis) as well as the nitrite concentration (in mg/L onsecondary y-axis) for three sets of input parameters (x-axis) from the new measurements.

47

4.3. Additional aspects to consider

4.3 Additional aspects to consider

The parameters measured during the pilot study are known to influence the ozone demand.In addition to these parameters, measurements were made for new parameters which couldinfluence the ozonation. These parameters, along with other aspects were then consideredfor improving the Ozone Dose(sim)-model.

4.3.1 New parameters

The parameters that were not measured during the pilot study, conductivity and Fe2+ weremeasured for three days to measure the levels over a longer period of time then for thethree tested doses. Conductivity was measured at a fairly similar level for all days. As nomeasurements were made during the pilot study it is unknown whether this parameter hasbeen altered and how much it impacts the ozonation. However, it is clear that conductivity,and thereby ions, can be detected in the wastewater.

No Fe2+ could be detected above the detection level of the method used, 0.02 mg/L, show-ing that no, or only a very small amount, of Fe2+ is present in the water into the ozonation.Hence, Fe2+ is not likely a contributing factor to the ozone demand as no ozone is requiredto oxidize Fe2+. [13, 21] Suspended solids were measured during the pilot study, but onlyat a few occasions. However, this parameter is known to influence the ozone demand andthe new measurements from the sampling days show significant variations for this parame-ter. Although, the average value from the new measurements for suspended solids is quitesimilar to the value from the pilot study.

Table 4.9: Parameters that are expected to influence the ozonation measured during the pilotstudy as well as the sampling days.

Parameter Pilot Study Day 1 Day 2 Day 3 AverageConductivity (mS/m) - 72.3 59.9 64.5 65.57Fe2+ (mg/L) - <0.02 <0.02 <0.02 -Suspended solids (mg/L) 12 6.9 28 7.5 14.13

Additionally, alkalinity which was not measured during the new measurements on the full-scale facility and only measured a few times during the pilot study would be of interest. Alka-linity increases when nitrite concentration decreases, which influence the ozonation processand thereby the ozone demand. Therefore, further measurements of alkalinity are of interest.[13, 18, 19]

4.3.2 Model improvements

There are parameters that are known to influence the ozone residual and thereby the ozonedemand that were not included in the MVA-models. The reason for this is the limited data,both from the pilot study and from the full-scale facility. In future, measurements can be donefor interesting parameters to investigated further. Moreover, the flow of the wastewater isknown to affect the ozone demand [13], but since there is a delay in flow compare to the otherparameters, see figure 4.1, problems arose when the flow was included in the multivariateanalysis.

As there is a delay in the variations of other parameters compared to the flow, using flow as apredictor in a multivariate analysis gives unexpected and unreliable results. The coefficientsfor all other parameters included in a PLS with flow get the opposite sign (plus/minus) towhat is expected and also the model fit is bad, with high p-values and low R-sq values.Hence, it is important to investigate the flow further to construct a function which adds the

48

4.3. Additional aspects to consider

effects of the flow on the ozone demand. This function could then potentially be added to theMVA1-model and thereby the Ozone Dose(sim)-model. However, other parameters vary asan effect of a varied flow, although with some delay, and part of the influence of flow mightbe captured by others parameters such as nitrite and COD(sim).

The MVA1- model which is the basis for the Ozone Dose(sim)-model includes ozone resid-ual, nitrite, turbidity, COD(sim) and ozone dose. However, including additional parameterscould further improve the accuracy in predicting the ozone dose at a given set of conditions(input parameters). Flow, DOC, conductivity and suspended solids would be relevant pa-rameters to include when improving the model.

For DOC, suspended solids and alkalinity there was not enough data available from the pilotstudy. Therefore, additional measurements on the full-scale ozonation are required beforethese parameters can be included in the model. For DOC the model can be modified witha coefficient for the relation of COD(sim) to DOC. However, a DOC coefficient determinedin a multivariate analysis would be preferable. Suspended solids show large variations,see table 4.9, and therefore including this parameter is very relevant as higher levels wouldcorrespond to a higher ozone demand. Although determining an accurate constant for anaverage value of suspended solids to add into the MVA1-model is somewhat difficult due tothe large variations. Moreover, alkalinity would be of interest due to an observed reversiblecorrelation to the nitrite concentration during the pilot study. [8]

Of the parameters that were not measured during pilot study, COD is included in the modelbased on the simulation data. However, if data from actual measurements were used in a newPLS the unexpected, positive coefficient might be altered to a negative value. Conductivitycould also be of interest to include in a model, as this parameter indicates that there are ionspresent in the influent wastewater to the ozonation that might be oxidized by ozone, givinga higher ozone demand. Although, additional measurements are required for this parameter,before it can be included in the model. On the other hand, Fe2+ was not present above thedetection level of 0.02 mg/L and at lower concentrations the effects on ozone demand fromthis parameter are minor and can be excluded in a model.

49

5 Conclusion

5.1 Conclusion

The main objectives were to verify the pilot study and to find the optimal ozone dose to reacha reduction of pharmaceutical residues, that is sufficient to eliminate negative environmentaleffects, at different conditions in Nykvarnsverket. This was achieved through measurements,by investigating which parameters influence the ozone demand and the correlation betweenthese parameters. Also, by developing and evaluating possible models for the ozone demandbased on data from the pilot study the main objectives were reached. The conclusions inrelation to these objectives are as follows;

• The reduction of pharmaceutical residues is similar to the pilot study, although slightlylower. Additionally, the linear relationship observed between the remaining residuesand ozone sensitive UVA left after ozonation for the dose trial is followed by the newmeasurements. This shows that the results from the new measurements on the full scalefacility can be used verify the pilot study results based on the reduction of pharmaceu-tical residues.

• The new measurements made on the full-scale ozonation showed an increase in concen-tration for several parameters. DOC, nitrite, COD and UVA were all measured at higherlevels before the ozonation. Also, the amount of ozone sensitive UVA left after ozona-tion was higher for the new measurements. Additionally, The COD concentrations fromthe new measurements are higher than the COD(sim) of 2014 form Linköpingsmod-ellen. Moreover, the on-line measurements showed that the nitrate concentration hadincreased significantly whilst flow, turbidity and temperature were at approximately atthe same levels as during the pilot study. The effects of the changes in these parametersis a slightly lower reduction of pharmaceutical residues.

• A regulatory strategy based on UVA could potentially be used as a control strategyand this should be tested and evaluated in the full-scale facility. The linear relationshipobserved from the dose trial for the pharmaceuticals of interest in this thesis is followedby the new measurements making this strategy promising.

50

5.1. Conclusion

• The three different models that were determined, the MVA1-model, MVA2-model andN-model were validated. Historic data from the pilot study was used for validationand the model which fitted the data best was selected for further analysis. The MVA1-model was deemed as the best and this model includes ozone residual, nitrite, turbidity,COD(sim) and ozone dose

• From the MVA1-model an Ozone Dose(sim)-model was constructed. From the OzoneDose(sim)-model a predicted ozone dose for each set of the input parameters isachieved. The variations in the dose compared to the input parameters nitrite, turbidityand COD(sim) for the validation data show that the model predict the ozone dose well.All parameters have the same variations as the predicted ozone dose, which means thatthe model can be said to predict the ozone dose accurately. The model was tested withinput data from full-scale measurements and the predicted ozone doses were deemedas accurate.

Based on these conclusions the main objectives of this thesis has been reached. The pilotstudy has been verified and a model which can accurately predict the ozone dose has beenconstructed. However, it can currently not be assured that the 90 % reduction of pharmaceu-ticals is received for the ozone dose predicted by the Ozone Dose(sim)-model at a given setof conditions.

51

5.2. Future Work

5.2 Future Work

• The accuracy of the Ozone Dose(sim)-model in predicting the ozone dose based on full-scale measurements should further be tested. Also, it is unknown if the ozone dosepredicted is sufficient to achieve the goal to reduce 90 % of the pharmaceutical residuesin the wastewater and future work is required.

• By including as much information as possible, the model prediction of the ozone dosebecomes more accurate. Additional parameters should be included into the OzoneDose(sim)-model in order to make it more accurate and hence, the risk of selecting adose that leads to over- or underdosing is reduced. Parameters that are of interest toinclude in the model in order to capture as much information as possible would beflow, DOC, suspended solid, alkalinity and conductivity. Measurements showed thatthere is conductivity in the water, this could mean that ions which consume ozone arepresent in the water, but further measurements are necessary. Moreover, both DOCand suspended solids are known to influence the ozone demand and additional mea-surements, in order to obtain enough data to include these parameters in the modelare recommended. Also, measurements of Fe2+ showed that the concentration was be-low the detection level of 0.02 mg/L. Hence, the conclusion is that Fe2+ is not a majorcontributing factor to the ozone demand.

• Based on the linear relationship between the ozone sensitive UVA left and the remainingpharmaceutical residues after ozonation a control strategy could potentially be devel-oped. This strategy would then ensure that the goal of a 90 % reduction of pharma-ceutical residues required to eliminate negative effects on the environment is achieved.Investigating additional pharmaceutical residues to see how if they follow the linearrelationship would also be of interest in developing this control strategy.

52

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A Appendix A

A.1 MATLAB script

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B Appendix B

B.1 Purpose, objectives and boundary conditions

The project will focus on the process of pharmaceutical residue treatment with ozone infull-scale (ozonation of pharmaceutical residues) at Nykvarnsverket, the wastewater treat-ment plant (WWTP) of TVAB in Linköping. The purpose of this project is to verify thatthe ozonation process works, to investigate how different ozone doses and loads will affectthe process, to optimize the ozone dose used in the process by evaluating different controlstrategies and formulate a model for the ozone demand.The main goal is to ensure that theozonation works in the full-scale process, to observe a correlation between ozone depletingsubstances and formulate a model for the ozone demand at different loads that can be usedat any WWTP as well as regulate the ozone dose using control strategies based on differentparameters.

The goals are:1. To ensure that the ozonation works in the full-scale process2. To observe a correlation between ozone depleting substances and formulate a model forthe ozone demand at different loads3. To validate results from a pilot study performed at Nykvarnsverket4. To regulate the ozone dose based on different parameters and evaluate different controlstrategies

The sub-goals are:1. Measurements performed on the full scale ozonation process at different ozone doses andloads (for all goals)2. Measurement results analyzed (for all goals)3. Full scale measurement results and pilot study results compared (for the third goal)4. Multivariate analysis performed (for the second goal)

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B.2. Time plan

To ensure that the progress can be measured the sub-goals are set as milestones giving thema time-frame. Additionally, the goals and sub-goals are presented as activities and hencecan be measured in terms of completion. However, there are boundary conditions where themeasurements are the critical step. If the measurements cannot be conducted this will resultin delays and potentially that all of the goals cannot be met. Moreover, there is a limit in theamount of measurements that can be performed and parameters that can be analyzed due tothe time restriction of 20 weeks and the resources available. Also, there is a known problemwith the cooling water filter in the ozonation process which gets clogged after approximately4 hours of running the process. This will have an impact on how and when the measurementscan be performed.

B.2 Time plan

The milestones are closely connected to the sub-goals and includes key examination activitiesand critical events. The time plan shows the main activities to be performed. Critical steps inthis project is primarily the measurements step. Problems with the equipment may arise andwithout experimental results, the main goal can’t be reached. To avoid this critical step, themeasurement step is planned to proceed for a longer time than expected, i.e. buffer time is inaccount to minimize the risk of lack of time.

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