DEGREE PROJECT IN ENVIRONMENTAL ENGINEERING, SECOND CYCLE, 30 CREDITSSTOCKHOLM, SWEDEN 2017

MODELLING AND FUTURE PERFORMANCE ASSESSMENT OF DUVBACKEN WASTEWATER TREATMENT PLANT

EMMANOUIL MILATHIANAKIS

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT

MODELLING AND FUTUREPERFORMANCE ASSESSMENTOF DUVBACKEN WASTEWATER

TREATMENT PLANT

Emmanouil Milathianakis

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TRITA LWR Degree Project ISSN 1651-064X LWR-EX-2017:01

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SUMMARY

The purpose of this work is the development of a computer model of the Duvbacken wastewater treatment plant in Gävle, Sweden under limited amount of data in order to simulate and predict the future performance of the plant based on which, possible solutions and recommendations for further research are proposed. The plant was initially designed to treat a maximum loading equal to 100,000 person equivalent (P.E.). However, the loading occurring from the increasing population in the community will soon surpass the design value and therefore the plant operator requested a permit for 120,000 P.E. Consequently, the performance of the plant at this loading should be investigated. At present, only the biological oxygen demand (BOD7) and total phosphorus (Ptot) emissions of the plant are regulated with a permit while a possible nitrogen (N) permit during the next years is raising questions about the future operations in the plant.

The thesis is composed of eight chapters, each of them dealing with different aspects of this work. Chapter One is introductory and presents the main concerns that triggered this work as also the concept of wastewater treatment and its necessity. Moreover, the purpose and method of this study are discussed.

Chapter Two explains the basic theoretical aspects related to the thesis such as the activated sludge process and the mechanism of biological and chemical phosphorus removal which are the main processes used for wastewater treatment in Duvbacken plant. In addition, the history of wastewater treatment modelling is presented along with the BioWin® model which was used for the simulations in this study. Finally, the basic steps in the wastewater treatment modelling process are explained.

Chapter Three presents the area around the Duvbacken wastewater treatment plant as also the eutrophic environmental conditions in the Inner and Outer Bay where the plant is discharging in. An extensive presentation of the plant configuration follows and each process in the plant is described. Moreover, the operational past of the plant until its present state is reviewed along with the plans for the future.

Chapter Four deals with the methodology followed in this work. This chapter does not follow the common methodology outline found in the majority of other studies but mostly presents how each procedure for this work was set up in order to produce valuable results. First, the methodology to overcome the significant lack of data is presented, which was one of the main issues encountered. Following, the main concept behind the sensitivity analysis is explained, which was used in order to develop the final model. Then, the procedure to develop the computer model for the wastewater treatment plant is described along with the two states under which the model can be used, namely steady and dynamic states. Finally, the scenarios investigated with the developed model are presented along with the methodology followed to set them up.

Chapter Five presents the results of the model sensitivity analysis, the final model of the plant along with the calibration and validation procedures and also the results of the future scenarios investigated.

Chapter Six is of particular interest as the proposals for the future development of the plant that arise from the scenarios in the previous section are discussed. In addition, the operation of the plant under a nitrogen permit is also discussed.

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In Chapter Seven the final conclusions of this work are presented followed by Chapter Eight where recommendations for further improvement of the tasks that could not be completely accomplished in this work are discussed.

Through this work it was proved that a computer model which can simulate the general performance of a wastewater treatment plant can be developed under a significant lack of valuable - in terms of modelling - data. The model sensitivity analysis which was used in an alternative manner proved to be a very useful tool under such conditions of data deficiency. The developed model predicted that the plant will be sufficiently operating at the loading of 120,000 P.E. while an increase in chemicals for phosphorus precipitation will be unavoidable. However, some additional proposals such as the installation of disc filters as a final treatment step or precipitation to take down possible peaks of organic loading were proposed in order to improve the plant operation. In case of a nitrogen (N) permit in the future it was concluded that the construction of some additional process tanks will be needed. The expansion of the plant could be minimized by installing membrane bioreactors (MBR) and introducing a full scale chemical precipitation of phosphorus.

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SUMMARY IN SWEDISH

Syftet med detta arbete är att utveckla en datormodell för Duvbackens avloppsreningsverk (ARV) i Gävle, Sverige. Modellen avser vattenbehandlingsprocessen vid anläggningen. Denna modell är baserad på en begränsad mängd driftsdata och befintliga volymer för simulering och framtida prestandautvärdering. Grundat på resultaten kan föreslås möjliga lösningar och rekommendationer för vidare forskning. Reningsverket var i början konstruerad för att behandla en maximal belastning av 100,000 personekvivalent (P.E). Belastningen från den ökande befolkningen i samhället kommer snart att överträffa detta dimensionerande värde. Anläggningens huvudoperatör, Gästrike Vatten AB avser att begära ett nytt tillstånd för Duvbackens ARV motsvarande 120,000 P.E. Som en konsekvens av detta har Gästrike Vatten valt att undersöka prestanda av reningsverket vid denna högre belastning. För närvarande är endast biologiska syrebehovet och totala fosforutsläppen av reningsverket regleras. Ett framtida krav som omfattar ett begränsat kväveutsläpp under de närmaste åren ger upphov till frågor om den framtida utformningen av Duvbackens ARV.

Examensarbetet består av åtta kapitel var och en av dem presenteras nedan.

Kapitel ett är inledande och presenterar de viktigaste frågorna som berörs i detta arbete. Avloppsrening och dess nödvändighet presenteras också. Dessutom syftet och förfarandet med denna studie redovisas.

Kapitel två förklarar de grundläggande teoretiska villkoren i samband med examensarbetet, såsom aktivslamprocessen och mekanismen av biologisk och kemisk fosforrening. Dessa är de viktigaste delprocesser som används för avloppsrening i Duvbackens reningsverk. Dessutom är historien om avloppsreningsmodellering presenterad tillsammans med BioWin® modellen, som användes i denna studie. Slutligen förklaras de grundläggande stegen i avloppsreningsmodelleringen.

Kapitel tre presenterar området runt Duvbackens reningsverk. En presentation av reningsverkets konfiguration följer och varje process beskrivs. Dessutom reningsverkets historiska utveckling presenteras. Slutligen redovisas de aktuella planerna för framtiden.

Kapitel fyra beskriver metoderna som används i detta arbete. En av de viktigaste frågorna i examensarbetet har varit att hantera den begränsade mängden på relevanta driftsdata. Metoden för att övervinna den begränsade mängden på data förklaras och en beskrivning av hur proceduren för att utveckla datormodell för avloppsreningsverket redovisas. Modellen tillåter att två olika driftsituationer kan studeras: Ett stabilt tillstånd och dynamiska tillstånd. Slutligen redovisas de undersökta scenarierna tillsammans med den utvecklade modellen.

Kapitel fem presenterar resultaten av den genomförda känslighetsanalysen. Den slutliga utarbetade modellen av reningsverket undersöktes tillsammans med kalibrering, validering och resultaten av framtidsscenarierna.

Kapitel sex är av särskilt intresse eftersom förslagen för reningsverkets framtida utveckling diskuteras. Dessutom diskuteras konsekvenserna av reningsverkets drift gestaltas under ett kvävetillstånd.

I kapitel sju presenteras slutsatser från detta arbete.

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I kapitel åtta redovisas rekommendationer för ytterligare preciseringar av frågeställningar som inte omfattas av detta arbete diskuteras.

Genom detta arbete har det visats att en datormodell kan simulera och bidra till den allmänna utvecklingen av ett avloppsreningsverk även om det finns begränsade mängd på data. Känslighetsanalysen som användes i detta arbete har visat sig vara ett mycket användbart verktyg, då underlagsdata bedöms som begränsat. Den utvecklade modellen pekar på att Duvbackens ARV kommer att kunna klara en föroreningsbelastning om ca 120,000 P.E. Givetvis kommer i detta fall en ökning av fällningskemikalier för en säkerställd fosforreduktion att att vara oundviklig. Några ytterligare förslag såsom installation av skivfilter som ett slutligt behandlingssteg, eller en kompletterande kemisk fällning för att reducera eventuella toppar av organisk belastning föreslås. Ett framtida krav på kväve (N) tillstånd kommer enligt studien att kräva att ytterligare processtankar kommer att behövas. Utbyggnaden av reningsverket skulle kunna begränsas genom att installera membran bioreaktorer (MBR) och införa en fullskalig kemisk fällning av fosfor.

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ACKNOWLEDGMENTS

The financial resources for the purchase of BioWin® software used in this work were provided from the Royal Institute of Technology (KTH) in Stockholm.

Foremost, I would like to express my sincere gratitude to my supervisor and examiner Prof. Elzbieta Plaza for assisting me with working with a master thesis in the sector of wastewater treatment which was my main interest and her continuous support throughout this work.

Besides my supervisor, I would like to thank Niclas Åstrand and Stig Morling from Sweco AB for sharing their valuable knowledge and professional expertise that greatly assisted my research as also for giving me the chance to work with this project in the modern workplace of Sweco AB headquarters in Stockholm.

I am also immensely grateful to my parents and friends for their continuous support especially during my hard times with this work.

Any errors in this work fall under my responsibillity and should not tarnish the reputation of the aforementioned esteemed people.

Author,

Emmanouil Milathianakis

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TABLE OF CONTENTS

Summary in English iii

Acknowledgments v

Table of Contents ix

Table of Figures xiii

List of Abbreviations xv

Abstract 1

1. Introduction 1 1.1. Problem statement 2 1.2. Wastewater and treatment necessity 3 1.3. Background 3 1.4. Purpose of the study 4 1.5. Method of the study 4

2. Theory related to the study 6 2.1. Activated sludge process 6 2.2. Phosphorus Removal 6

Biological phosphorus removal 6 2.2.1. Chemical phosphorus removal 7 2.2.2.

2.3. Sludge fermentation 7 2.4. COD fractions, Kinetics and Stoichiometry 8 2.5. Activated sludge modeling 8

Models 8 2.5.1. The Activated Sludge Models – History and development 9 2.5.2. The BioWin model 10 2.5.3. Steady-state and dynamic-state models 10 2.5.4. Model calibration and validation 11 2.5.5. The advantages of having a wastewater treatment model 11 2.5.6. Modelling process 11 2.5.7.

3. Duvbacken wastewater treatment plant 13 3.1. Location and environmental condition 13 3.2. Background 13 3.3. Earlier studies 14 3.4. Present state 14 3.5. Future goals 16 3.6. Plant configuration 16

Process description 17 3.6.1.

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4. Methods 20 4.1. Data manipulation 20

Required datasets for successful modeling 20 4.1.1. Presentation and manipulation of available datasets 21 4.1.2.

4.2. Sensitivity analysis 23 Theory and Considerations 23 4.2.1. Sensitivity coefficients 24 4.2.2.

4.3. Model development 26 4.4. Steady-state model 27

Steady-state model calibration 27 4.4.1. Steady-state model validation 28 4.4.2.

4.5. Dynamic-state model 28 Dynamic-state model calibration 28 4.5.1. Dynamic-state model validation 28 4.5.2.

4.6. Future scenarios 29

5. Results and Discussion 30 5.1. Sensitivity analysis 30 5.2. Steady-state model 34

Steady-state model calibration 34 5.2.1. Steady-state model validation 36 5.2.2.

5.3. Dynamic-state model 37 Dynamic-state model calibration 37 5.3.1. Dynamic-state model validation 39 5.3.2.

5.4. Predictions of other significant processes of the WWTP 41 Waste sludge production 41 5.4.1. Chemical precipitation 42 5.4.2. Sludge fermentation (Side stream hydrolysis) 43 5.4.3. Anaerobic digester 45 5.4.4.

5.5. Scenarios 45 Scenario 1: Plant emissions at 120,000 P.E. 45 5.5.1. Scenario 2: Plant emissions at 120,000 P.E. and maximum inflow since 5.5.2.

2010 46 Scenarios outcome 46 5.5.3.

6. Future proposals 48 6.1. Proposal without nitrogen permit 48 6.2. Proposal with nitrogen permit 48 6.3. Complementary proposals 49

7. Conclusions 50

8. Further recommendations 53 8.1. Improvement of the model 53 8.2. Sampling frequency 54 8.3. Assessment of the use of chemicals 54

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8.4. Performance evaluation of disc filters against full-scale chemical P removal 54

References 55

Appendix 1 – Comparison between required and available data I

Appendix 2 – Creation of complete datasets II

Appendix 3 - Estimation of input data for scenarios VI

Appendix 4 – Biowin® parameters mentioned in this work IX

Appendix 5 – Dynamic model calibration and validation results for CODtot, Ntot, NH4-N and PO4-P XI

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TABLE OF FIGURES

Figure 1. Duvbacken WWTP location (star) along with the Inner and Outer Bay (Inre and Yttre fjärden respectively). 2

Figure 2. Schematic overview of the different steps in activated sludge model development. 12 Figure 3. Current configuration of Duvbacken wastewater treatment plant (2016). 16 Figure 4. The set-up of the BioWin® model. 26 Figure 5. Simulated (bars) and measured values of the effluent water variables and their

95% confidence bands for the steady-state calibrated model. 36 Figure 6. Simulated (bars) and measured values of the effluent water variables and their

95% confidence bands for steady-state model validation. 37 Figure 7. Dynamic-state simulation results of the calibrated model for the effluent Ptot

using data of January 2016. 38 Figure 8. Dynamic-state simulation results of the calibrated model for the effluent BOD7

using data of January 2016. 38 Figure 9. Dynamic-state simulation results of model validation for effluent Ptot using data

of March 2016 (The bottom chart presents the whole range of values whereas the top chart allows for a closer view of the lower values). 40

Figure 10. Dynamic-state simulation results of the model validation for effluent BOD7 using data of March 2016. 40

Figure 11. Calibrated dynamic model results for CODtot. XI Figure 12. Calibrated dynamic model results for Ntot. XI Figure 13. Calibrated dynamic model results for NH4-N. XII Figure 14. Calibrated dynamic model results for PO4-P. XII Figure 15. Validated dynamic model results for CODtot. XIII Figure 16. Validated dynamic model results for Ntot.. XIII Figure 17. Validated dynamic model results for NH4-N. XIV Figure 18. Validated dynamic model results for PO4-P. XIV

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LIST OF ABBREVIATIONS

AAO Anaerobic ammonia oxidizers AOB Ammonia oxidizing biomass ASM Activated sludge model BNR Biological nutrient removal BOD Biological oxygen demand COD Chemical oxygen demand HRT Hydraulic retention time IAWQ International association of water quality MBR Membrane bioreactors N Nitrogen NH4-N Ammonium NO2-N Nitrite NO3-N Nitrate NOB Nitrite oxidizing biomass OHO Ordinary heterotrophic organisms P Phosphorus PAO Polyphosphate accumulating organisms PHA Polyhydroxyalkanoates PO4-P Phosphate RAS Return activated sludge rbCOD readily biodegradable COD SRT Solids retention time SVI Sludge volume index TDS Total dissolved solids TKN Total Kjeldahl nitrogen -tot Total TS Total solids TSS Total suspended solids VFA Volatile fatty acid WAS Waste activated sludge WWTP Wastewater treatment plant

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ABSTRACT

Duvbacken wastewater treatment plant in Gävle, Sweden, currently designed for 100,000 person equivalent (P.E.) is looking for a new permit for 120,000 P.E. due to the expected increase of the population in the community. Moreover, the recipient of the plant’s effluent water was characterized as eutrophic in 2009. The plant emissions are regulated regarding seven days biological oxygen demand (BOD7) and total phosphorus (Ptot) emissions. Yet, there is no available computer model to simulate the plant operations and investigate the emissions of the requested permit. However, it was uncertain if the available data would be sufficient for the development of a new model.

A model of the plant was eventually developed in BioWin® software under a number of assumptions and simplifications. A sensitivity analysis was conducted and used conversely than in other studies. The sensitivity analysis was conducted for the un-calibrated model in order to indicate its sensitive parameters. The parameters of substrate half saturation constant for ordinary heterotrophic organisms (KS) and phosphorus/acetate release ratio for polyphosphate accumulating organisms (YP/acetic) were finally used for model calibration. Following, the model validation confirmed the correctness of the calibrated model and the ability to develop a basic model under data deficiency.

The new model was used to investigate a loading scenario corresponding to 120,000 P.E. where plant emissions that meet the current permits were predicted. Some suggestions proposed were the installation of disc filters in order to further reduce the effluent phosphorus and BOD precipitation in cases of high influent concentrations. In case of the application of a nitrogen (N) permit, the installation of membrane bioreactors and a full-scale chemical P removal was proposed as an alternative that will require a smaller footprint expansion of the plant.

Keywords: Activated sludge, Data deficiency, Model calibration, Phosphorus removal, Sensitivity analysis, Wastewater

1. INTRODUCTION

Wastewater treatment processes account for the largest industry in terms of volumes of the raw materials treated. The large treatment plants that operate for this purpose usually encounter issues related to outdated process control systems, the constant population increase and the strict emissions permits regulated by legislation.

Duvbacken wastewater treatment plant (WWTP) located in the municipality of Gävle in Sweden and operated by Gästike Vatten, was constructed in the late 60’s and has been retrofitted a couple of times since then (Morling, 1988). Since 2004 the plant operates with activated sludge and biological phosphorus removal (BNR) and seldom with chemical phosphorus precipitation when continuous high concentrations are detected in the effluent water. Currently, the regulation is controlling the effluent seven days biological oxygen demand (BOD7) and total

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phosphorus (Ptot) emissions of the plant while there is a possibility for introducing a nitrogen (N) permit in the future.

The numerous and complex biochemical reactions occuring in a WWTP are usually impossible to predict based on common system observation and experimentation. Therefore the possession of a computer model that as accurately as possible simulates the plant’s processes is an important and significantly useful tool for the plant operator and the growing sector of environmental protection.

1.1. Problem statement Duvbacken WWTP is currently discharging in the Inner Bay which is connected to the larger Outer Bay before it reaches the Gulf of Bothnia (Fig. 1). Both Inner and Outer Bays were characterized as eutrophic in 2009, with the plant being partly responsible for this situation along with other smaller plants, industries and runoff in the area. Moreover, Gävle community is expecting a population increase from around 99,000 inhabitants in 2016 to 120,000 in 2030 and 150,000 in 2050 (Gävle kommun, 2016). The loading generated by the continuously increasing population in the community is expected to soon exceed the loading design value of the plant. More specifically, the plant is designed to treat a maximum loading equal to 100,000 person equivalent (P.E.) if each person in the community is producing 70 gBOD7/d. Therefore, the plant operator has requested a permit for 120,000 P.E. and the emissions of the plant while receiving this loading have to be investigated.

Figure 1. Duvbacken WWTP location (star) along with the Inner and Outer Bay (Inre and Yttre fjärden respectively).

Regarding Duvbacken WWTP, there was not yet a complete computer model that could predict the future emissions of the plant or the effects of any process change. Therefore, the development of a model was deemed

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necessary since the population increase is expected to push the plant to its limits in the next decades, especially regarding P emissions.

An unexpected and unprecedented - compared to previous studies - data deficiency occurred in the course of this work, challenging even more the attempt to develop a model that could sufficiently simulate the processes in the plant and predict its future behavior.

1.2. Wastewater and treatment necessity The waste production from human activities is unavoidable and a significant part of this will end up as wastewater varying in quality and quantity, based on geographical and social factors (Henze & Comeau, 2008). Domestic and industrial wastewater along with surface runoff from roads and infiltration water produce enormous amounts of wastewater requiring treatment every day (Gray, 2004).

Preventing environmental pollution and protecting public health are the two major reasons for treating wastewater. Water-borne diseases can arise from human faeces due to the high content of bacteria and viruses. Regarding environmental pollution, organic enrichment of the water reservoirs endanger the flora and fauna due to deoxygenation while a high nutrient content of the treated effluents can lead to eutrophication problems (Gray, 2004).

A large contribution to the improvement of the wastewater treatment sector as also to the operation and optimization of wastewater treatment plants arose by the development of mathematical models – or else, simplifications of the reality – comprehensively describing wastewater treatment processes.

1.3. Background Activated sludge models have become quite complex upon the years since they were first developed. This implies that apart from adequate expertise, a large amount of data is also required in order to develop a precise and comprehensive model. This in turn implies numerous experiments and measuring campaigns which are costly and time consuming. Numerous authors in published studies highlight the importance of sufficient data in the modelling process. On the contrary, it is not uncommon that some important data are usually missing from each study and various assumptions have to be made or the guideline values have to be followed. The data deficiency in this work was much larger compared to other modelling studies in the literature reviewed and therefore the development of a model under these circumstances became quite challenging.

Sensitivity analysis has been extensively used in the modelling process, confirming whether the correct modifications in the model have been done. However, using the sensitivity analysis in the opposite manner as was done in this study has not been practiced in a large number of studies and therefore it was a worth-investigating topic.

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Regarding the plant of Duvbacken, a quite small number of published works has been identified while none of them included the development of a user-friendly model that could further assist the consultants and the plant operator with any desired predictions.

1.4. Purpose of the study This study was conducted in order to:

• Successfully develop, calibrate and validate an activated sludge model (ASM) in the BioWin® software for Duvbacken wastewater treatment plant that could efficiently simulate its processes and predict its future behavior.

• Assess the predictability of the model developed with a limited amount of data.

• Set the basis for a complete, more advanced and precise model development for Duvbacken WWTP in order to more accurately examine the future performance of the plant and contribute to the improvement of its efficiency.

• Perform a sensitivity analysis of a complex ASM (in this study, BioWin® ASDM) which has hardly ever been performed.

• Predict the future emissions of the plant based on scenarios of interest.

• Make recommendations based on the findings of this study for the improvement of the model and the operation of the plant.

1.5. Method of the study First, an academic license of the BioWin® v5.0 was purchased from EnviroSim Associates Ltd. in order to later develop the computer model. Following, the author acquired knowledge and skills on the manipulation of the software during a sufficient period of time. On the 22nd of July 2016 a guided field visit at Duvbacken WWTP and Gästrike Vatten headquarters with the process engineer of the plant took place. Meanwhile, a thorough literature review regarding wastewater treatment aspects related to the study and previous modeling projects was taking place.

Data acquired from the database of Gästrike Vatten as also data files provided by Sweco AB, were elaborated and analyzed in MS Excel in order to acquire information needed to develop the model in BioWin® as also to create comprehensive input datasets which partly covered the gap created by the data shortage. A comprehensive model sensitivity analysis based on the mean square sensitivity measure and the normalized sensitivity coefficient, model calibration and validation followed. Further simulations in BioWin® were performed to investigate the future emissions of the plant.

Finally, after discussions with the project’s supervisor and considering the overall results of the study, the final conclusions were drawn and further recommendations were proposed.

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2. THEORY RELATED TO THE STUDY

2.1. Activated sludge process The field of activated sludge process is vast and therefore only a brief presentation is performed in this study, enough to provide the basic principles.

The activated sludge process is the most widely used biological wastewater treatment process for both domestic and industrial wastewaters. It uses the naturally occurring bacterial flocs and microorganisms, suspended within the wastewater, for water purification. The process belongs to the category of the suspended growth processes since the microorganisms are dispersed in the wastewater, in contrast to the attached growth processes. The process relies on the utilization of pollutants as food source by the microorganisms under aerobic conditions, which occur from the continuous supply of oxygen resulting in dissolved oxygen (DO) in the wastewater. The types of microorganisms involved in this process are:

• anaerobic (organisms that do not need DO) • aerobic (organisms that must have DO) • facultative (organisms that can exist with or without DO)

The suspended mixture of wastewater and microorganisms, also called mixed liquor suspended solids (MLSS), is supplied with oxygen which is consumed by the organisms and also maintains the liquor in suspension. The dense microbial population uses the organic pollutants and converts them to new biomass and byproducts. Following, the mixed liquor flows to a clarifier where the biomass is allowed to settle and form a layer at the bottom of the tank, called sludge. A portion of the settled biomass is constantly returned through a stream - also known as return activated sludge (RAS) stream - to the treatment process in order to provide the adequate amount of microorganisms and maintain the process operational. The portion of sludge not used in the return stream is removed as waste activated sludge (WAS) for further processing downstream. (Gray, 2004; Department of Environmental Quality of Michigan, 2011).

2.2. Phosphorus Removal

Biological phosphorus removal 2.2.1.

In biological phosphorus (bio-P) removal process, alteration of anaerobic and aerobic stages favors polyphosphate accumulating organisms (PAOs), which are the heterotrophic bacteria that are responsible for this process. First, in the anaerobic stage, PAOs convert readily available organic material such as volatile fatty acids (VFAs) which are mainly acetate and propionate, to energy-rich carbon polymers called polyhydroxyalkanoates (PHAs). More specifically, PAOs use energy to take up VFAs and convert them to PHAs. This energy is generated through the breakdown of

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polyphosphate molecules which results in P release. Following, in the aerobic zone, PAOs oxidize the stored PHAs to obtain energy through an excess P uptake which is used for growth. At the settling zone downstream, biomass containing all the P is removed with sludge while the clear water is removed from the plant (Jeanette A. Brown et al. 2005).

Chemical phosphorus removal 2.2.2.

Chemical P removal is taking place in the form of precipitation followed by sedimentation. During P precipitation, soluble P is transformed to a particulate form and these particles are removed by sedimentation. The cations commonly used are iron, aluminum and calcium (Jeanette A. Brown et al. 2005).

2.3. Sludge fermentation The efficiency of bio-P removal depends much on the amount of VFAs available to PAOs during the anaerobic treatment process. VFAs are a form of readily biodegradable carbon sources, namely readily biodegradable COD (rbCOD) which can be already present in sufficient concentrations in the influent wastewater. However, the influent VFA content is sometimes insufficient and then VFAs can be produced on-site through the fermentation of slowly biodegradable COD, present in the primary or secondary return activated sludge (RAS) (Jabari et al., 2016). There are three different ways that can be used for sludge fermentation; biological, chemical or thermal sludge fermentation. This study deals with the biological sludge fermentation, in which anaerobic bacteria are grown in fermenter tanks where they hydrolyze (acid digestion) the slowly biodegradable COD and transform it into smaller molecules of mainly acetate and propionate which along with butyrate and valeriate constitute VFAs. In order to maximize VFA production the fermenter solids retention time (SRT) must be short enough to maintain acid digestion and avoid the conversion of the organic material to methane. (WEF, 2005)

For a plant that operates with BNR, if the desirable effluent P concentrations are below 1 mg/L or below, the influent wastewater must contain a COD:P ratio of 45:1 or higher. In cases that this cannot be sustained, secondary sludge fermentation may be practiced in order to return a portion of high VFA content liquor to the biological treatment.

The on-site sludge fermenting process can be beneficial in the following ways (WEF, 2005):

• The WWTP becomes independent of the outside VFA sources • On-site VFA generation is often of lower cost than chemicals • It shortens the anaerobic zone detention time since the fermenter

supernatant introduced, includes VFAs ready for uptake • It decreases the sludge load of the anaerobic sludge digestion process

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A disadvantage of activated sludge fermentation may be the high nutrient content of the fermentate. However, this negative effect can be outweighed by the overall benefit of the VFA production.

2.4. COD fractions, Kinetics and Stoichiometry Defining the fractions of the organic substrate is one of the most important aspects of wastewater treatment modeling. COD is either biodegradable or unbiodegradable. The unbiodegradable matter can in turn be soluble (<1 mμm) or particulate (1 mμm – 100 μm); in both cases this matter is considered inert and named SI and XI respectively. The biodegradable fraction of the organic matter is the substrate that is utilized by the microorganisms in the wastewater treatment process and can also be soluble (SS) or particulate (XS). Particulate biodegradable matter is too large to pass through the cells of the microorganisms present in wastewater and must first be decomposed to smaller particles. This procedure of decomposition takes time and therefore XS is called slowly biodegradable substrate. On the contrary, soluble biodegradable matter is readily available to be taken up by microorganisms. Microorganisms are considered to belong to the slowly biodegradable particulate fraction (Chai, 2008).

Inorganic substrate comprises small molecules which are soluble. Inorganic substrate includes oxygen, nitrogen, ammonium (NH4-N), nitrate (NO3-N), nitrite (NO2-N) and phosphorus (Chai, 2008).

Defining the fractions of COD is a very important aspect of wastewater treatment process design and modeling. First, the readily biodegradable soluble COD in the influent wastewater determines the ability of a plant to implement biological phosphorus removal. Also, the expected sludge production is influenced by COD fractions. However the COD fractions do not occur in a particular pattern and they are unique for each WWTP. Therefore it is very important to conduct laboratory experiments for COD fractionation before any attempt of modelling a WWTP.

The kinetic and stoichiometric parameters describe the reactions that take place during the wastewater treatment. Briefly describing, kinetic parameters can contain bacterial growth and decay rates, hydrolysis factors, saturation constants for the substrate and several other processes’ rates. Stoichiometric parameters describe the fractions of inorganics in biomass, the yield of bacteria and several other types of yields. These parameters are of equal significance as the COD fractionation and require experimental campaigns before conducting any WWTP modeling.

2.5. Activated sludge modeling Models 2.5.1.

The term “model” in this work, refers to mathematical models where equations are defined to relate inputs, outputs and characteristics of a system. Regarding wastewater treatment, this system is defined by the wastewater treatment plant or a single process in it. An accurate model

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allows for the behavior prediction, optimization and control of the system. Moreover, a model contributes to the reduction of practical experiments, reduces the costs, saves time and also allows the investigation of completely theoretical systems that do not exist in reality (Jeppsson, 1996).

The wastewater treatment models contain a large number of kinetic and stoichiometric parameters. These parameters comprise microorganisms yields, their growth rates etc. Despite their large number and complexity, as shown from many studies these parameters do not change considerably for systems that treat municipal wastewater. On the contrary, large changes in their values usually indicate an improperly characterized system (Melcer, 2004).

Another characteristic of the models is the different number of state variables and processes each one describes. A state variable is described as a model constituent used for a descriptive representation of the system (Dochain & Vanrolleghem, 2001). To name a few, state variables can include the system’s biomass, flow, temperature, endogenous products and others. On the other hand, processes may include the actions that take place in a system such as the ammonia removal rate, VSS destruction rate, the air flow rate etc.

The Activated Sludge Models – History and development 2.5.2.

The time period between the promotion of the activated sludge method for water treatment and the establishment of the first models was quite long. The main reasons for this slow transition period were the conflicting nature of the many hypotheses for the mechanistic explanation of the processes, the difficulty of their accurate mathematical representation and the nature of the systems on which the models were developed (Liwarska-Bizukojc et al., 2013).

As early as the 1920s and until the 1960s, numerous alternative hypotheses about organic matter removal by activated sludge were proposed. It was recognized that physical, biological and chemical mechanisms might be responsible for the purification of the wastewater, including theories such as the coagulation, adsorption and colloid theories. It is noteworthy that a biological mechanism that purifies water by ingestion and assimilation of organic matter while it synthesizes it into living material (microorganisms), was already proposed since 1923. Regarding the different fractions of organic material, it wasn’t until 1955 that researchers began to consider its particulate and soluble components. Operational difficulties and new processes such as nutrient removal, led to increased use of the models and this, in turn, to the need for the creation of mathematical models incorporating all the involved mechanisms (Liwarska-Bizukojc et al., 2013).

In 1983, the International Association of Water Quality (IAWQ) reviewed the existing models in order to create the simplest mathematical model that could realistically predict all the water purifying processes; finally resulting in ASM1 (Liwarska-Bizukojc et al., 2013). Following ASM1, the

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models ASM2 to incorporate nitrogen and biological phosphorus removal, ASM2d to incorporate denitrifying PAOs and finally ASM3, a next generation activated sludge models’ tool, were created (Henze et al., 2000).

Nowadays, ASM-based models are included in most commercial and non-commercial wastewater simulation software while their development is going on at a quite fast pace (Henze et al., 2000).

The BioWin model 2.5.3.

In BioWin®, developed by EnviroSim Associates Ltd., most types of wastewater treatment systems can be configured since it includes a large variety of process modules. BioWin® uses a general Activated Sludge/Anaerobic Digestion (ASDM) model, usually referred as the BioWin® General Model. This model is a combination of ASM1, ASM2d and ASM3 proposed by IAWQ coupled with an anaerobic digestion model (ADM).

BioWin® incorporates fifty (50) state variables and sixty (60) process expressions in contrast with the thirteen (13) state variables and eight (8) process expressions or the twenty (20) and nineteen (19) of ASM1 and AMS2 respectively. This complete model approach frees the user from having to map one model’s output to another model’s input which reduces the complexity of building full plant models. BioWin® also incorporates seventy-eight (78) kinetic and fifty-four (54) stoichiometric parameters. Kinetic and stoichiometric parameters are divided into categories related to the groups of microorganisms taking part in biological wastewater treatment processes which are Ammonia Oxidizing Biomass (AOB), Nitrite Oxidizing Biomass (NOB), Anaerobic Ammonia Oxidizers (AAO), Ordinary Heterotrophic Organisms (OHO), PAO, methylotrophs, acetogens and methanogens. Kinetic parameters include two additional categories which include pH parameters and switching functions. In addition BioWin® allows the user to work with the standard IAWQ models (ASM1, ASM2, ASM2d, ASM3) if desired (BioWin Help Manual).

Steady-state and dynamic-state models 2.5.4.

Steady-state model

The steady-state model has to mimic the performance of the plant on a long-term scale ignoring the sudden process disturbances (Vanrolleghem et al. 2003). The model is using averaged data which are not time-dependent and is not describing the hydraulic details of the plant. The steady-state is reached by assuming constant influent flow rate and concentrations. The steady-state model calibration can be used to determine the parameters responsible for the long-term behavior of the plant, e.g. the yield or decay rate of the bacteria (Petersen et al. 2002).

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Dynamic-state model

Dynamic simulation shows the time-varying system response based on the time-varying influent loading to the system (BioWin 5.0 Help Manual). A dynamic-state model is using time-dependent data and is required to describe and predict more short-term and dynamic situations as also the complexity of the system that includes a large number of biological and biochemical processes (Petersen et al. 2002).

Model calibration and validation 2.5.5.

Model calibration

Model calibration is defined as the alteration of model’s parameters so that the simulated results fit the measured values for a certain set of data from the WWTP being studied (Petersen et al. 2003).

Model validation

The validity of the model is tested by running it using a data set which was not used in the model calibration step. In case that the model fails to predict the effluent values with a “reasonable” precision for this data set it is then advised to iterate the calibrated values or conduct additional lab-experiments (Vanrolleghem et al. 2003).

The advantages of having a wastewater treatment model 2.5.6.

A model is usually built for individual plant processes but where it becomes most useful is when the entire plant processes are incorporated in a single model. A wastewater treatment model can be a powerful tool to the plant operator for a variety of tasks such as (Nutt et al., 2004):

• Planning: where the model is used to investigate future anticipating influent or effluent requirements that arise from increased plant loading and stricter regulations.

• Design: a calibrated model is commonly used to design wastewater treatment plants and ensure that they will be effective during future changes in loadings or regulations.

• Operations: the model can be used to evaluate changes in plant performance; especially when one or more process units are out of service for maintenance purposes.

• Plant optimization: it is one of the most important uses of a model since it gives the operator the ability to evaluate the whole plant performance while a change to a single process is applied.

• Costs saving: any plant optimization that can lead to costs and energy savings can be first tested with the use of a model.

• Risk management: a model can help the plant operator to take the adequate safety measures when special circumstances are expected.

Modelling process 2.5.7.

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Figure 2. Schematic overview of the different steps in activated sludge model development.

The modelling process is usually following the steps presented in Fig. 2. Steps 9 and 10 are coupled with a steady and dynamic-state model validation step followed by a steady and/or dynamic-state model sensitivity analysis step respectively, in order to confirm that the correct parameters were calibrated. However, the steady and dynamic-state sensitivity analysis results tend to be very similar therefore the sensitivity analysis of one model is usually sufficient. However the modelling process can stop before the dynamic-state steps. Depending on the case study and its purpose, it is not uncommon that only the steady-state model is developed.

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3. DUVBACKEN WASTEWATER TREATMENT PLANT

3.1. Location and environmental condition Duvbacken wastewater treatment plant is located in the eastern part of Gävle, about 2 km from the city center. The plant is adjacent to an industrial area on the west and also to a nature reserve on the east. The discharge point of the plant is located about 400 m into the Inner Bay and then flows out through the Outer Bay, finally reaching the Gulf of Bothnia (Fig. 1). Several industries operate close to the bay including the port of Gävle. Added to that, three smaller wastewater treatment plants also operate in Gävle municipality which are located in Axmarby, Norrsundet and Hedesunda.

Gavleån and Testeboån rivers and pulp-paper industries BillerudKorsnäs AB and Stora Enso AB along with Duvbacken WWTP are the major sources of nutrients and organic substances discharging in the Inner Bay. However, the plant represents only the 15 and 37% of P and N respectively, of the amount occurring by Gavleån and Testeboån to the Inner Bay. On the other hand, regarding metals, Duvbacken WWTP is the major contributor in the Inner Bay exceeding by far the other sources. Moreover, BillerudKorsnäs AB is responsible for the largest amount of organic material in the Bay, Stora Enso for P and Duvbacken WWTP for N. The areas around the Inner and Outer Bay are much industrialized areas including also a port and other buildings.

According to the latest status assessment from 2009 the Inner and Outer Bay were characterized as eutrophic and it was considered technically and economically impossible to achieve good ecological status by 2015, therefore the deadline has been set to 2021.

3.2. Background The city of Gävle started the planning of a wastewater treatment plant in the 1950’s with a feasibility study presented in 1958 which proposed a mechanical/biological treatment for approximately 120,000 inhabitants, including some major food industries. The plant was designed with a capacity of 100,000 P.E. making it the largest in the community compared to the other three plants with a combined capacity of 106,500 P.E.

The plant would include pre-treatment, primary settlers, aeration basins and final clarifiers as also two large 7,500 m3 digesters for anaerobic sludge digestion. Digested sludge was initially planned to be dewatered and incinerated. However, due to ineffective operation, the dewatering and incinerating units were replaced by decanter centrifuges and lime treatment respectively.

The plant is operational since 1967 and during 1975 - 76, government grants favored the construction of a post-precipitation step following the

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biological treatment, which along with the updated, highly efficient fine bubble aeration system led to energy savings between 25 – 40 %.

Later in 1980’s Duvbacken plant was retrofitted with the addition of a pre-precipitation step and an anaerobic digester unit and some improvements on the pre-treatment step. (Morling 1988).

3.3. Earlier studies A rather small number of previous studies have been performed for Duvbacken WWTP (based on internet results).

Chai (2008) in her work “Modeling, estimation and control of biological wastewater treatment plants” partly dealt with the creation of two ASMs and parameters estimation regarding Duvbacken WWTP. However, mainly due to lack of on-line measurements of the influent water composition the models could not be precisely developed. The fitting of the models to real data from the plant was not great but it was at least satisfactory. More specifically, the fit was quite good for Ptot and phosphate (PO4) - which along with BOD are the only effluent parameters controlled by environmental regulation in the specific area – while the fit of other parameters including nitrogen was less good. Regarding the first model built on Matlab, Qian Chai mentions that it is relatively rigid with respect to possible input signals while the second model was built on Modelica modeling language. However both Matlab and Modelica platforms are not user-friendly enough and the models cannot be easily reproduced and improved by other researchers or the operators and so, the need of an easier-to-use and improve model is arising.

Another thesis study by Eva Kumpulainen for Sweco AB, regarding the evaluation and optimization of the sidestream hydrolysis process at Duvbacken WWTP provided useful information that have been taken into account for the present work. However, the aforementioned thesis does not regard the simulation of the plant processes.

3.4. Present state Gästike Vatten AB is the operator of Duvbacken WWTP. They are a union of water companies (Gästrike Vatten AB, Gävle Vatten AB, Hofors Vatten AB, Ockelbo Vatten AB and Älvkarleby Vatten AB) which are responsible for water supply and sewage treatment in the municipalities of Gävle, Hofors, Ockelbo and Älvkarleby (”www.gastrikevatten.se”). Approximatelly 86,000 people are connected to Duvbacken WWTP which is currently receiving a daily loading of around 89,000 P.E. and removes the BOD and P through biological processes. The incoming BOD design capacity ranges at 7,000 kg/d and Ptot at 300 kg/d. In cases where the performance of the biological system is poor (effluent P > 0.5 mg/L) or when there is an increased inflow, P is chemically precipitated with ferric chloride (FeCl3). The plant has a design inflow capacity of 1,900 m3/h but has proved to be efficient up to 2,500 – 3,000 m3/h. The design values

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are presented in Table 1 whereas the basic loading characteristics of the plant are presented in Table 2.

Table 1. Design flow and pollution load of Duvbacken wastewater treatment plant.

Parameter Unit Loading

Connected Person Equivalent

P.E. 100,000

Design Flow Q m3/h 1,900

Bio-P Flow Qmax m3/h 3,000

BOD7 kg/d 7,000

Ptot kg/d 300

Table 2. Duvbacken WWTP loading characteristics (Gästrike Vatten Miljörapport 2010-2015) Parameter Units 2010 2011 2012 2013 2014 2015

Connection according to BOD7 P.E. 88,469 92,181 77,677 93,198 73,390 68,958

Including Industry

6,000 6,000 6,000 6,000 5,250 5,250

Connected People People 86,000 82,700 83,157 84,242 84,644 85,450

Average Flow m3/d 38,433 35,105 38,680 35,463 31,919 33,128

BOD7 kg/d 6,193 6,453 5,452 6,524 5,137 4,827

Ptot kg/d 178 167 436 260 260 340

Ntot kg/d 1,104 1,024 2,274 1,693 1,910 1,992

At present, the plant effluent is controlled by regulation for the content of Ptot and BOD7. The permit for BOD7 is at 8 mg/L or 120 tons/yr and for Ptot at 0.3 mg/L or 6 tons/yr. The permits and the annually produced amounts for the previous years are presented in Table 3.

Table 3. Permit values and annually produced concentration and mass for Ptot and BOD7 (values in brackets denote the produced amount including the overflow stream).

Parameters Unit Permit Produced amounts

2010 2011 2012 2013 2014 2015

BOD7 mg/L 8 3.7 (4.7) 5.04 (5.6) 4.6 (4.97) 5.3 (6.1) 3.8 (3.8) 3.8 (3.9)

BOD7 ton/yr 120 50 (66.1) 63 (71.9) 65 (70.2) 66 (78.9) 44 (44) 46.1 (46.6)

Ptot mg/L 0.3 (0.4 before 2012)

0.36 (0.40) 0.41(0.43) 0.33 (0.35) 0.28 (0.34) 0.39 (0.39) 0.27 (0.27)

Ptot ton/yr 6 (7 before 2012)

4.7 (5.6) 5.16(5.57) 4.58 (4.95) 3.55 (4.40) 4.55 (4.55) 3.26 (3.28)

As is also evident in Table 3, the average daily BOD7 loading is approaching the design loading of 7,000 kg BOD7/d. This is also the case

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for the daily loading of P which occasionally exceeds the design loading during some years. However the total mass of BOD7 and Ptot in the effluent water lie below the permits until today.

3.5. Future goals At present, Gävle has a population of 99,110 people (March 31, 2016) and around 86,000 are connected to Duvbacken WWTP. The community estimates a population increase to around 120,000 inhabitants until 2030 and 150,000 until 2050, which also implies an increase in workplaces, social services, schools, sports, communications and at least 10,500 housing units (“www.gavle.se”). Therefore the plant operator has requested a loading permit for 120,000 P.E. Considering the aforementioned, it is evident that there will soon be a need for increase of the plant’s loading capacity or the reconsideration of the effluent permit values. Added to that, the introduction of a permit on effluent nitrogen will require the construction of nitrification – denitrification tanks in the plant.

3.6. Plant configuration The plant has undergone process upgrades and has been retrofitted several times since it first became operational. Currently, the main aspects of the plant are mechanical, biological and chemical treatment as also sludge fermentation, digestion and handling. A schematic outline of the current configuration at Duvbacken WWTP is presented in Fig. 3. Following, design information for each process is presented in Table 4 and finally each process is briefly described in the next section.

Figure 3. Current configuration of Duvbacken wastewater treatment plant (2016). *The present configuration was designed by Emmanouil Milathianakis in Autodesk AutoCAD (2016).

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Table 4. Design information of Duvbacken WWTP processes (values in brackets indicate the number of tanks). Process Area (m2) Volume (m3)

Sand trap (2) 100 740

Primary sedimentation (6) 1,760 4,300

Anaerobic (3) 450 2,355

Aerobic 1 (3) 1,350 7,065

Aerobic 2 (2) 650 2,460

Aerobic 3 (5+9) 600 2,400

Secondary sedimentation (9) 2,970 11,900

Fermenter (1) 300 1,300

Primary sludge thickener (1)* 170 700

Biological sludge thickener (1)* 170 700

Digester (2) 600 3,600

Sludge holding tank (1) (not illustrated) 315 1,260

Overflow tank (6) (not illustrated) 3,850 7,940

*Both illustrated as a single tank.

Process description 3.6.1.

Mechanical treatment

Mechanical treatment takes place in the following order:

• Screening: screening removes objects such as rags, paper, plastics and metals to prevent damage and clogging of the downstream equipment. Screenings are pressed to reduce in size and are later disposed in a container.

• Sand trap: sand and grit are removed by quick sedimentation in two tanks with a total volume of 740 m3. This step allows the separation of sand, gravel and other large organic particles that are much heavier than the biodegradable solids. The removed solids are further washed out to return any organics adsorbed on them downstream, so as to enhance the biological phosphorus removal process. Similarly to screenings, washed material here is also disposed in a container.

• Primary sedimentation: the process takes place in 6 equal sized tanks with a total volume of 4,300 m3. The tanks are designed in such a way that primary sludge hydrolysis is stimulated; however not with remarkable results, as is reported in a previous research from Kumpulainen (2013).

Biological treatment

Biological treatment comprises one anaerobic and three aerobic steps.

• Anaerobic and aerobic 1: these two treatment steps include three process trains, each train including one anaerobic and three aerobic

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tanks connected through openings below the tank walls while the return activated sludge stream (RAS) is discharging into the anaerobic tanks. The total volume of the anaerobic and aerobic tanks is 2,355 and 7,065 m3 respectively. The dissolved oxygen in the aerobic 1 stage, provided by blowers at the tanks’ bottom, varies between 1 and 2 mg/L.

• Aerobic 2: comprises two tanks of a volume of 2,460 m3 in total. Surface air blowers allow in average 1-1.5 mg/L oxygen to dissolve in the mixed liquor.

• Aerobic 3: this step is maintaining the mixed liquor in suspension before entering the final sedimentation tanks and includes five larger and nine smaller sized tanks with a total volume of 2,400 m3.

Sludge handling

Sludge is either used as RAS in the biological treatment or it is led, through the waste sludge (WAS) stream, to the sludge thickeners and then to the anaerobic digesters.

• Primary sludge occurs in the primary sedimentation tanks and is pumped up into two thickeners downstream.

• Secondary sludge is produced while secondary sedimentation takes place in nine – out of 10 existing – equal sized tanks with a total volume of 11,900 m3. In cases of poor biological treatment performance or high P in the influent wastewater (>0.5 mg/L), FeCl3 is used for chemical precipitation of P at this step. A portion of the secondary sludge is wasted (WAS) and controls the SRT in the system and the largest portion left (RAS) is partly fermented and returned back to the anaerobic step.

• Thickened sludge occurs in gravity settlers (thickeners) and consists of primary sludge and WAS. At the same time the supernatant from the thickeners returns back to the main stream, discharging in the primary sedimentation tanks.

• Digested sludge is anaerobically digested thickened sludge which occurs in the two large digesters, 1,800 m3 each, used for methane gas production. According to the process engineer of the plant the digested sludge level is maintained steady at 5 m in total depth in the digesters and the effluent gas flow varies around 164 m3/h with a 62% methane content. The digested sludge is led into a holding tank (not illustrated) in order to be later centrifuged. At this point, polymer is added to the sludge to improve the dewatering properties. Finally, the dewatered sludge is carried by trucks away from the plant for composting and landfill material covering.

Fermenter

One of the ten secondary sedimentation tanks is operating as a sludge fermenter for VFA production since 2011, in order to enhance bio-P removal and reduce the use of chemicals. The 1,300 m3 fermenter tank is fed by the secondary sludge. The sludge feed and discharge from the

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fermenter is around 1,500 m3/d which results to an equal SRT and hydraulic retention time (HRT) of 20.8 to 20.9 hours.

Overflow tanks (not illustrated)

The old post-precipitation tanks are now occasionally operating as holding tanks in order to relieve the system from overflows. In case that the inflow is higher than, approximately, 2,800 m3/h, the excess flow is received by these tanks. These are six, equal sized tanks with a total volume of 7940 m3. When the water volume in the tanks reaches at around 7,000 m3, then P is removed through chemical precipitation by the addition of FeCl3. The treated water is directly discharged to the plant’s outflow and the remaining sludge is led to the sludge thickeners. However, when the water volume accumulated in the tanks is lower than 7,000 m3, there is neither outflow nor chemicals addition and they just serve as holding tanks. Then, when the plant flow returns to ordinary levels, all the mixed liquor is gradually returned into the primary sedimentation tanks.

As pointed out from the plant’s process engineer, water is usually observed in the tanks even when it is not a period that overflows occur. This is either infiltrated water from the tanks’ walls or rainwater and melting snow.

Chemical treatment

FeCl3 is used in cases of poor biological treatment performance or high influent P while a polymer is used for dewatering purposes.

Regarding chemical P precipitation, FeCl3 is mostly added in the secondary sedimentation tanks or in the overflow tanks (when full). Although it is uncommon, sometimes, FeCl3 is also added in primary sedimentation tanks. As for sludge dewatering, polymer addition takes place right before the sludge is centrifuged. The amount of chemicals annually used in the plant are presented in Table 5.

Table 5. Annual use of chemicals in Duvbacken wastewater treatment plant. Point of use Units 2010 2011 2012 2013 2014 2015

Precipitation (secondary sed.) ton/yr 33.1 80.6 66.4 85.3 344.3 122

g/m3 2.5 4.7 4.8 6.8 30 10

Precipitation (overflow tanks) ton/yr 21.6 72.6 29 41.9 0 1.3

g/m3 38 - - - - -

Precipitation (primary sed.) ton/yr 22.5 0 0 0 0 0

Polymer (Dewatering) ton/yr 18 16.1 16.2 17.1 17.4 14.8

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4. METHODS

4.1. Data manipulation Comprehensive data is of the most crucial factors for a case study identical to the present one. Unfortunately, no sufficient data were available for Duvbacken WWTP. In this chapter it was attempted to clarify the sources where the data were acquired from as also the assumptions that were made in order to successfully complete this work.

Required datasets for successful modeling 4.1.1.

Henze et al. (1995) reported that wastewater characterization is crucial in the process of modelling since the quality of the predictions depends much on it. In addition, Melcer (2004) also stated that it has a significant impact on the performance of the activated sludge unit, particularly regarding nutrient removal systems. Petersen et al. (2002), in their study about the evaluation of ASM1, reviewed various sources and summarized the information needed for a successful model creation. The set of information gathered, is presented below:

1. Design data, e.g. reactor volumes, pump flows, aeration capacities.

2. Operational data, e.g. flow rates of influent, effluent, recycle and waste flows as also pH, aeration and temperatures.

3. Characterization for the hydraulic model, e.g. the results of tracer tests.

4. Characterization for the settler model, e.g. zone settling velocities at different mixed liquor concentrations.

5. Characterization for the biological model of wastewater concentrations of WWTP influent and effluent (as well as some intermediate streams between the unit processes), e.g. suspended solids, COD, TKN, NH4-N, NO3-N, PO4-P, etc.

6. Sludge composition, e.g. volatile suspended solids (VSS), COD, N and P content.

7. Reaction kinetics, e.g. growth and decay rates. 8. Reaction stoichiometry, e.g. yields.

For better understanding the required against the available data are presented in Appendix 1. Added to the aforementioned, COD fractionation in its inert and particulate biodegradable and unbiodegradable fractions, also not available in this study, is crucial for the model development.

Vanrolleghem et al. (2003) mention that the lack of information regarding the operational changes introduce large uncertainties in understanding the system behavior which are difficult to interpret and consider in the final plant model.

Liwarska-Bizukojc & Ledakowicz (2011) in their work about the determination of kinetic and stoichiometric parameters of ASMs,

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summarize the experiments that can be conducted for this purpose. It can be noticed that the experiments’ list is quite long which implies that at the same time there is an increase of the model confidence, the costs and the duration of a study.

In the study of Petersen et al. (2002) it was also noticed that the kinetic parameters obtained from lab-scale experiments were not significantly different from the models default parameter set. However, the lab-scale results offer an extra confirmation of the parameter set of the calibrated model which in turn increases the quality and confidence of the model.

As is already mentioned, the present study was conducted with significantly limited amount of data and no measuring campaigns or lab-scale experiments were conducted. Therefore, the methods required for data acquisition are not presented since none of them was possible to be practiced.

Presentation and manipulation of available datasets 4.1.2.

The shortage of available data, the limited on-line measurements at the WWTP and the impossibility of conducting experimental campaigns are of the most significant factors that affected this study and need to be clarified. These factors add a degree of uncertainty and decrease the confidence of the acquired results. In some cases, the creation of additional values in order to obtain complete datasets for dynamic simulations, was inevitable. These values were created by using arbitrary methods and data correlations which are described in Appendix 2. However, the created datasets appeared realistic and matched the existing real values for the time period that these were available. It is also important to mention that in some cases there was a mismatch between the values of the same variable (flows, concentrations etc.) provided by two different sources, fact that adds up to the uncertainty of the datasets.

Following, the available as also the created datasets required as per the list in section 4.1.1. are presented:

1. Design data

Regarding the design data, the volume and the surface area of each treatment process were only available (Table 4).

2. Operational data

The flow data provided by Gästrike Vatten concerned the time period from January until June of 2016. The data represent the average daily flow values of the following streams:

• Inflow • Primary settler – thickener sludge stream • Thickener supernatant – primary settler stream • RAS stream • Overflow tank outflow • Fermenter inflow

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• WAS stream • Thickener – digester sludge stream

Effluent flow data were not available since there is no effluent flow meter at the plant. However a rough estimation of the average weekly effluent flow was done by correlating the average weekly effluent P mass production and concentration.

The pH value for the influent water was reported by the process engineer to vary around 7.3.

According to Kumpulainen (2013) the plant personnel reported an average temperature of 10-12 ℃ for the period January – March.

The aeration levels in the bioreactors were reported by the process engineer to vary around 1-2 mg/L dissolved oxygen (DO).

3. Characterization of the hydraulic model

Regarding the steady-state model, characterization of the hydraulic model is not essential since each process is simulated as an ideal completely mixed tank. On the other hand, information about the hydraulic model are necessary for the development of the dynamic model so that it simulates the real flow conditions of the plant. The characterization of the hydraulic model is usually achieved with tracer tests. However, that was not possible for this study and therefore the same model configuration as for the steady-state model was used for the dynamic-state model. What differentiates the two model states in this study is that dynamic data are used in the steady-state model in order to perform a dynamic-state simulation.

4. Characterization of the settler model

Settling velocities were not available for this study and also there were no data regarding the sludge blanket height in the primary and secondary settler.

5. Characterization of the biological model

The biological characterization of wastewater is usually conducted by chemical analyses of samples from various points of interest throughout the plant. For this study no sampling and chemical analyses were possible. More specifically, most of the data were acquired from the Duvbacken WWTP annual reports which are compiled from the plant’s chemical engineer. More detailed descriptions follow below:

• Influent: influent wastewater characteristics are of the most important information needed for a model calibration. However, for this study COD and Ptot concentrations were available as weekly average values twice or once a month and sometimes no values at all for a specific month were available. The situation for BOD7, Ntot, NH4-N and TOC was even worse with only one or zero values per month. No other variables are measured at the plant’s influent.

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• In order to conduct a dynamic model simulation in BioWin®, a dataset including continuous values among others for COD, Ptot and TKN is needed. Arbitrary methods relating input and output values were adopted along with relations between the variables reported by Henze & Comeau (2008) in order to create continuous datasets for those parameters. The new values were then assumed as real in favor of the dynamic simulation. At this point it is worth-mentioning that the new values exactly match the real ones for the time periods they are available. Finally, the outliers were replaced with more realistic values. The methodology followed and the new datasets are presented in Appendix 2.

• Effluent: The effluent dataset was the most comprehensive. Daily measurements were available from the plant’s operation logbook for the concentrations of NH4-N, NO3-N, Ptot and PO4-P. Effluent COD was reported as a weekly average twice per month and BOD7 along with Ntot as a daily measurement, four times a month.

• COD fractions: Information about the components of COD were not available neither at Gästike Vatten nor at the laboratory of the plant. Knowledge about COD components is crucial for the operation of a plant as also for the set-up of a model. However, it was not possible to conduct respirometric tests or other COD analysis.

• Additional data: more data were acquired from annual reports conducted by Gästike Vatten for the years 2010-2016 (first quarter of 2016). Data to evaluate the operation of the fermenter were found in the work of Kumpulainen (2013). Finally, literature values were also considered from various sources.

6. Sludge composition: the sludge production and composition were

available as annual average values for the recent years which were used for comparison to the predictions of the model.

7. and 8. Reaction kinetics and stoichiometry: no data were available

for both parameter categories. For that reason it was attempted to maintain the default values used by the BioWin® software.

4.2. Sensitivity analysis Theory and Considerations 4.2.1.

Sensitivity analysis provides the modelers the ability to evaluate to what extent the parameters modified in the model calibration can influence the model outputs. A small variation in a parameter’s value that causes a large variation in the model response, indicates a highly sensitive parameter (Liwarska-bizukojc & Biernacki 2010).

Hu et al. (2014) in their work about calibration and validation of an ASM refer that most of the studies emphasize the role of the sensitivity analysis in the process of model calibration. Continuing, Vanrolleghem et al. (2003) mention that the calibration and determination of all the model parameters is expensive and time consuming. They conclude that the information acquired from a sensitivity analysis should be used in the

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model calibration to minimize the calibration efforts and optimize the overall procedure.

The procedure usually followed begins with laboratory experiments for the determination of the values of various kinetic and stoichiometric parameters followed by the incorporation of these measurements to the model (model calibration) and then, a model sensitivity analysis to investigate and verify if the parameters modified during the model calibration were realistic, indeed influencing the model outputs on a significant level.

In this work, model calibration was impossible since no information about the kinetic and stoichiometric parameters was available. It has to be emphasized that in absence of significant information, it would be risky-to-impossible to identify which parameters may be crucial for the model output. Therefore, the sensitivity analysis was used in the opposite manner as was also done in the work of Hu et al. (2014), meaning that first, a sensitivity analysis was conducted for the un-calibrated model in order to indicate the most sensitive parameters, followed by the model calibration where some of the indicated sensitive parameters were modified. Using the sensitivity analysis before the model calibration in such cases is a useful method which leads the modeler towards a much smaller set of parameters. In some studies, experiments are then conducted just for this smaller set of parameters, minimizing the costs and time spent. When it is not possible to conduct experiments, such as in the present study, the identification of the parameters for calibration among the smaller set can be based on any information available. Information may regard the types of industries contributing to the organic loading of the plant, the temperature at the plant, the performance of the plant processes and others. Through this information it is sometimes possible to identify which parameters may be affected and need to be calibrated. However, calibrating the model entirely based on the sensitivity analysis would import a degree of bias to the model. Therefore, the least possible parameters were calibrated in order to avoid any case of bias.

Sensitivity coefficients 4.2.2.

According to US EPA (1987) guidelines the normalized sensitivity coefficient (Sij) represents the percentage change in the output variable (yi) to a 1% change in each model parameter (xi):

Sij = │

ΔyiyiΔxixi

│

In this work, the percentage of 10% was applied to the model parameters. Regarding Sij, the significance levels are considered as follows: (i) Sij < 0.25 indicates a parameter with no significant influence on the model output, (ii) 0.25 ≤ Sij < 1 indicates an influential parameter, (iii) 1 ≤ Sij < 2 means that a parameter is very influential and (iv) Sij ≥ 2 indicates an extremely influential parameter (Petersen et al., 2003).

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Another sensitivity analysis measure used is the mean square sensitivity measure (δjmsqr) introduced by Brun et al. (2002). This measure is assessing the individual parameter importance with regard to the changes that it causes to the output variables as a whole:

δjmsqr = �

1n� Sij2n

i=1

A high δjmsqr value indicates that a parameter has a significant influence on the model results, while a value of zero means that a parameter does not affect the model results.

Regarding the present work, the following six variables were taken into account in the calculations for Sij and δjmsqr: COD, BOD7, Ptot, PO4-P, Ntot and NH4-N. Sensitivity analysis is normally conducted for the calibrated model. However, as discussed in the previous section, the sensitivity analysis in this study was first performed for the un-calibrated and then for the calibrated model in order to examine if the sensitive parameters set will differ significantly. Regarding whether the analysis will be conducted using steady or dynamic data, the majority of similar studies mention that the sensitive parameters sets identified are similar, or slightly differ, in both cases. To mention some, Hu et al. (2014) and Liwarska-Bizukojc et al. (2011) in their work about the calibration of a complex ASM reported that all the parameters sensitive in the steady state calibration occurred to be sensitive in the dynamic calibration too. Similarly, Petersen et al. (2002) also identified the same parameters to be sensitive during both, steady and dynamic states sensitivity analysis. Taking into account the previous statements, a similar result should be also expected for the present study.

Hu et al. (2014) reported that five out of eight kinetic and stoichiometric parameters determined as influential in a previous study of Weijers & Vanrolleghem (1997) were also found to be influential in their work. Exactly the same statement was drawn in the work of Liwarska-Bizukojc & Biernacki (2010) who show that their results coincide with the ones from Weijers & Vanrolleghem (1997) too. Considering the aforementioned, similarities were also expected in the sensitivity analysis results of this study.

The effluent variable values of the un-calibrated steady-state model and the averaged effluent variable values of the un-calibrated dynamic-state model were used as initial values for the sensitivity analyses. The effluent variable values after each change of a parameter were then compared to these initial values.

A sensitivity analysis was conducted for the un-calibrated steady and dynamic state models as also for the calibrated steady-state model in order to identify any differences between their results. The sensitivity analyses tests results were elaborated using the MS Excel 2013 software and are presented in section 5.1.

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4.3. Model development Figure 4 illustrates the model set-up as it was developed in BioWin®. As is evident, in steady-state models each process is represented by a single, completely-mixed tank. On the contrary, in dynamic-state models each process is represented by as many tanks as needed in order to simulate the hydraulic behavior of the plant. However, it has to be noted again that in absence of the hydraulic model of the plant, the development of a dynamic-state model was not possible and therefore the following configuration of the steady-state model was used for the dynamic simulations. Whenever insufficient or no data were available for a specific process (e.g. removal rates of settlers), the default or literature values were used. Following, the basic information that should be mentioned about the model set-up are presented:

Figure 4. The set-up of the BioWin® model.

• Screenings and sand removal were modeled as a single tank with the removal characteristics of both processes.

• The primary sedimentation process includes a primary sludge fermentation mechanism in the real plant. In this case, fermentative conditions are developed in the sludge layer at the bottom of the primary clarifiers in order for the complex organics to hydrolyze to VFAs. However, in the work of Kumpulainen (2013) it was observed that the contribution of extra VFAs from this process was of minor significance. Considering the aforementioned and also due to the complete lack of data for this process, the primary sludge fermentation was not considered in the model. According to EPA (1997) the TSS removal efficiency of the primary settlement tanks is 50-70% and the BOD removal is 20-50%. This is also the case for the primary settler in the model; the TSS removal was set at 65% and the BOD removal varied around 34%.

• Aerobic 1 and 2 bioreactors were modeled with 1.5 mgDO/L as was mentioned by the plant’s process engineer and aerobic 3 with 2 mgDO/L.

• The secondary settler removal capacity was set by default at 99.8% TSS. The sludge blanket height is usually maintained between 30-90cm (Department of Environmental Quality of Michigan, 2011) and

Influent Screening/ Sand

Grit Storage

Anaerobic Aerobic1 Aerobic2 Aerobic3 Effluent

Sludge

Primary Settler

Fermenter Mixing tank

Secondary Settler

Digester

Thickener

27

so, the sludge blanket in the secondary settler was assumed close to 90cm.

• An anaerobic sludge fermenter was modeled in BioWin® as a non-aerated bioreactor with the kinetic parameter of anaerobic hydrolysis factor increased from 0.04 to 0.5 for this specific element (EnviroSim online support).

• The sludge gravity thickener removal rate retained at 99.8% which was the default value.

• No changes were also applied at the default values of the anaerobic digester except for the temperature value that was set at 37℃ according to the plant’s process engineer.

• The RAS and WAS streams were adjusted each time with regard to the available flow data for the time period simulated. In general the RAS flow rate (including WAS) varied between 42 and 54% of the plant inflow; WAS ranged around 1,400 m3/d. These streams could also be managed by applying a controller to the WAS/RAS splitter so as to maintain the SRT of the plant equal to the desired value (usually 5-6 days).

4.4. Steady-state model

Steady-state model calibration 4.4.1.

As long as the model was developed as presented in the previous section the step following was the model calibration, meaning the alteration of relevant parameters indicated by the un-calibrated model sensitivity analysis so that the values of the modeled effluent variables match the measured ones. As was mentioned in section 4.2.1., the results of the sensitivity analysis of the un-calibrated model were used in favor of the model calibration; therefore, the kinetic and stoichiometric parameters needed were calibrated, in order to match the desired results.

The target was to match the averaged effluent data of COD, BOD7, Ntot, NH4-N, Ptot and PO4-P for the month of January 2016. As model inputs, the averaged created values for January 2016 (Table 6 and Appendix 2) were used along with the averaged measured RAS, WAS and flow rates which resulted to an SRT of 6 days.

Table 6. Input parameter (averaged created) values for the steady state model calibration and validation. Parameter January (calibration) March (validation)

Flow 31,093 40,342

Total COD (mgCOD/L) 448 297

Total Kjeldahl Nitrogen (mgN/L) 48 33

Total P (mgP/L) 13.3 9

Nitrate N (mgN/L) 1 1

pH 7.3 7.3

28

Alkalinity (mmol/L) 6 6

ISS Influent (mgISS/L) 15 15

Calcium (mg/L) 80 80

Magnesium (mg/L) 15 15

Dissolved O2 (mg/L) 0 0

Due to the low number of available effluent variable values for COD, BOD7, and N but also due to the increased uncertainty for the input data, the predicted effluent values of the model were considered as acceptable if they fall in the range of the 95% confidence intervals of the measured effluent values (99% confidence intervals were also examined) (Table 11).

Steady-state model validation 4.4.2.

The calibrated model was evaluated against the predictions of the model using data of March 2016 (Table 6). It has to be mentioned that the RAS and WAS rates for March were different, resulting to an SRT of 5 days. All the other parameters remained the same. The same evaluation method as for the model calibration was used, using the 95% confidence intervals of the output variables.

4.5. Dynamic-state model

Dynamic-state model calibration 4.5.1.

Normally, the set-up of dynamic model differs from the steady-state model. This means that each process is not represented by a single completely mixed tank but from multiple tanks that simulate the hydraulic behavior of the plant. However, in the present work, in absence of information to set-up the hydraulic model, the same model configuration as for the steady-state (Figure 4) was used for the dynamic-state simulations. Similarly to the steady-state model, the created values of January 2016 were used (Appendix 2). Here, it has to be emphasized that the dynamic calibration does not aim to predict the precise values of an individual output variable, but to predict the trend of its changes (Hu et al. 2014).

Dynamic-state model validation 4.5.2.

Similarly to the steady-state model validation, data from March 2016 were used in order to validate the dynamic model. As for the dynamic model calibration, the dynamic model validation aimed to predict the trends of changes of the measured effluent variables’ values.

29

4.6. Future scenarios According to Gävle community, the population growth is expected at 120,000 people by 2030. At present the population is around 99,110 people (March 31, 2016) and around 86,000 are already connected to the Duvbacken WWTP. However there is neither information regarding the amount of people that will be served by the plant by 2030 nor about the future industrial development plans in the area. The current concern of the plant operators is to investigate the effluent emissions of BOD and P at 120,000 P.E. In order to investigate this scenario, estimations about the values of the influent flow, BOD, Ptot and Ntot by the time that P.E. will be equal to 120,000 were necessary in order to use them as inputs to the newly developed model. The methodology followed for these estimations can be found in Appendix 3. The average per person influent values of BOD7, Ptot and Ntot for the years 2010-2015 are presented in Table 7. The same scenario was also investigated using the maximum plant inflow since 2010.

Table 7. Measured influent BOD7, Ptot and Ntot values per connected person. 2010 2011 2012 2013 2014 2015 Average

g BOD7/p,d 72 78 66 77 61 56 68.4

g Ptot/p,d 2.07 2.02 5.23 3.09 3.07 3.97 3.24

g Ntot/p,d 12.8 12.4 27.3 20.1 22.6 23.3 19.8

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5. RESULTS AND DISCUSSION

5.1. Sensitivity analysis It is already mentioned that in this study the sensitivity analysis was used in an alternative way – preceding the model calibration instead of following it – in order to assist the identification of the sensitive parameters of the model. In the common procedure, various parameters of the plant are determined with laboratory experiments and thereafter they are calibrated in the model. Then, sensitivity analysis of the calibrated model follows in order to verify if the sensitive parameters were indeed calibrated. In this study, the model was calibrated using the un-calibrated model sensitivity analysis results. However, sensitivity analysis was also conducted for the steady-state calibrated model in order to identify possible changes in the sensitive parameters sets and also to compare them to results of previous studies which state that the calibrated model sensitivity analysis was more reliable than the un-calibrated ones.

The sensitive parameters are the ones for which the normalized sensitivity coefficient Sij was higher than 0.25. In total, 26 parameters were identified as sensitive for the un-calibrated steady state model (Table 8). More specifically, 13 parameters can be classified as influential (0.25 ≤ Sij < 1) and 13 as extremely influential (Sij>2). Seven out of 13 extremely sensitive parameters were associated with PAOs, three with OHOs, two with the switching functions and one with pH. The influential role of the model parameters associated with PAOs in ASMs and ASM-based models was also emphasized in the work of Makinia et al. (2006), Brun et al., (2002) and Petersen et al. (2003). Liwarska-Bizukojc & Biernacki (2010) mentioned in their work that the effluent concentration of Ptot was affected by 11 parameters, most of them associated with PAOs. This statement is also confirmed in the present work since all the extremely influential parameters were associated with PO4-P which is the main component of effluent Ptot.

Based on the results of the un-calibrated steady-state model sensitivity analysis, the model calibration was performed. The least possible parameters were calibrated for the model to meet the measured effluent values. These parameters were the substrate half saturation constant for OHOs (KS) and the phosphorus/acetate release ratio for PAOs (YP/acetic) (the calibration process is discussed in Section 5.2.1.).

The steady-state calibrated model was then used to perform an additional sensitivity analysis in order to evaluate possible changes compared to the results of the steady-state un-calibrated model. In Table 8 it can be observed that the sensitive parameters for the steady-state calibrated model do not significantly differ from those of the un-calibrated one. More specifically, 27 parameters in total were identified as sensitive for the calibrated model. 14 can be characterized as influential, nine as very influential and four as extremely influential. 21 parameters were similar for both models.

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Table 8. Values of the Si,j for the most sensitive (Si,j≥0.25) parameters of the un-calibrated (left) and the calibrated (right) BioWin® ASDM steady-state model.

Un-calibrated steady-state model

Parameters

Si,j

Calibrated steady-state model

Parameters

Si,j

BOD7 Ptot PO4-P

BOD7 Ptot PO4-P

Common

Common

Kinetic

Kinetic

Hydrolysis rate (kh)* 0.387

Hydrolysis rate (kh)* 0.527 0.326

Anaerobic hydrolysis factor (AD) 0.504

AOB

AOB

Kinetic

Kinetic

Max. spec. growth rate (μmaxA)* 0.417

Max. spec. growth rate (μmaxA)* 0.445

OHO

OHO

Kinetic

Kinetic

Max. spec. growth rate (μmaxH)* 0.669 0.417 5

Max. spec. growth rate (μmaxH)* 1.002 0.475

Substrate half sat. (KS)* 0.563

Substrate half sat. (KS)* 0.824 0.267

Aerobic decay rate (bH)* 0.528 0.417

Aerobic decay rate (bH)* 0.809

Fermentation half sat.* 0.417

Fermentation rate 0.653

Stoichiometric

Fermentation half sat.* 0.326

Yield (aerobic) (YH)* 0.634

Fermentation growth factor (AS) 0.564

Yield (fermentation, low H2) 0.417

Stoichiometric

Yield (fermentation, high H2)* 0.417 5

Yield (aerobic) (YH)* 0.433 1.988

H2 yield (fermentation low H2) 0.417

Yield (fermentation, high H2)* 1.306

Propionate yield (fermentation, high H2)* 0.417 5

Propionate yield (fermentation, high H2)* 0.303 2.433

Yield (anoxic) (YH,anoxic) 0.417

P in biomass 0.653

PAO

PAO

Kinetic

Kinetic

Max. spec. growth rate, P-limited* 0.417 5

Max. spec. growth rate 0.289 1.899

Substrate half sat.* 0.417 5

Max. spec. growth rate, P-limited* 1.691

Cation half sat. 0.417

Substrate half sat.* 1.543

Aerobic/anoxic decay rate* 0.417 5

Substrate half sat., P-limited 0.564

Anaerobic decay rate* 0.417 5

Aerobic/anoxic decay rate* 1.662

Stoichiometric

Anaerobic decay rate* 0.386

Aerobic P/PHA uptake* 0.317 0.417 5

Sequestration rate 0.564

Yield of PHA on sequestration* 0.423 0.417 5

Stoichiometric

P/Ac release ratio* 3.333 35

Aerobic P/PHA uptake* 0.307 0.676 5.045

Yield of low PP* 0.833 5

Yield of PHA on sequestration* 0.330 0.694 5.163

32

Un-calibrated steady-state model

Parameters

Si,j

Calibrated steady-state model

Parameters

Si,j

BOD7 Ptot PO4-P

BOD7 Ptot PO4-P

pH

P/Ac release ratio* 3.508 27.478

Kinetic

Yield of low PP* 0.623

OHO low pH limit (anaerobic)* 0.417 5

pH

Switches

Kinetic

Kinetic

OHO low pH limit (anaerobic)* 1.454

Aerobic/anoxic DO half sat. 0.417

Switches

PolyP half sat. 0.417

Kinetic

VFA sequestration half sat.* 0.417 5

VFA sequestration half sat.* 0.534

P uptake half sat.* 0.417 5

P uptake half sat.* 1.573

*these parameters are similar for the un-calibrated and calibrated model.

In the work of Petersen (2000), six out of 14 parameters identified as sensitive are also characterized as sensitive in this work for both calibrated and un-calibrated models [(yield coefficient for OHOs under aerobic conditions (YH), maximum specific growth rate of OHOs under aerobic conditions (μmaxH), maximum specific growth rate of AOB (μmaxA), aerobic decay rate for OHOs (bH), substrate COD half saturation constant (KS), hydrolysis rate (kh)]. Hu et al. (2014) identified eight in total sensitive parameters in their study, five of which were also identified here as sensitive for the calibrated and six for the un-calibrated model [YH, yield coefficient for OHOs under anoxic conditions (YH,anoxic) (only for un-calibrated model), μmaxH, μmaxA, bH, KS]. In addition, Weijers & Vanrolleghem (1997) found a reduced parameter set for ASM1 which included [YH, yield coefficient for AOB (YA), μmaxH, μmaxA, bH, KS, oxygen half saturation constant for AOB (KOA) and anoxic growth factor (ηanoxic,H)]. Greater similarities were identified with the work of Liwarska-bizukojc & Biernacki (2010). More specifically 14 out of 17 parameters characterized as sensitive in their model were also found to be sensitive in this study for both models [μmaxA, μmaxH, KS, bH, YH, maximum specific growth rate of PAOs under phosphorus limiting conditions (μmaxP-limited), PHA half saturation constant (KS,PAO), amount of P stored per unit of PHA oxidized in aerobic conditions (YP/PHA,aerobic), amount of PHA stored when 1mg of acetate or propionate is sequestered (YP/PHA,seq), amount of P released for 1 mg of acetate sequestered in the form of PHA (YP/acetic), fraction of P stored in releasable poly-P form (YlowPP), P uptake half-saturation constant (KPP), (maximum specific growth rate of PAOs (μmaxPAO), substrate (PHA) half-saturation constant under phosphorus limiting conditions (KS,P-limited) only for calibrated model)]. This greater number of similarities probably occurs due to the fact that their study used the BioWin® v3.0 ASDM model which implies that almost the same parameters were under examination. Added to that, BioWin® has a greater number of parameters than other ASMs, hence, the identification of more sensitive parameters that do not exist in other commercial

33

software is reasonable. The discussed similarities above are clearly presented in Table 9.

Table 9. Comparison of the sensitivity analysis results with previous studies.

Identified sensitive parameters

Common with un-calibrated model

Common with calibrated model

Petersen (2000) 14 6 6

Hu et al. (2014) 8 6 5

Weijers & Vanrolleghem (1997) 8 5 5

Liwarska-Βizukojc & Biernacki (2010) 17 12 14

Following, the values of the mean square sensitivity measure (δjmsqr) earlier introduced in Section 4.2.2., were calculated in order to identify the parameters with the highest influence in the model outputs for both model states (Table 10). The model parameters are presented in decreasing order so that the parameters with the higher influence can be found at the top of the list. It has to be mentioned that for better legibility only the top 10 parameters are listed.

Table 10. Importance rankings of kinetic and stoichiometric parameters based on δjmsqr for the un-calibrated (left) and the calibrated (right) BioWin® ASDM steady-state models. Un-calibrated steady-state model Calibrated steady-state model

Parameter δjmsqr Parameter δjmsqr

P/Ac release ratio*+ 13.29 P/Ac release ratio*+ 11.31

Yield of low PP+ 1.917 Yield of PHA on sequestration*+ 2.131

Max. spec. growth rate (OHO)* 1.914 Aerobic P/PHA uptake*+ 2.082

Yield of PHA on sequestration*+ 1.904 Propionate yield (fermentation, high H2)* 1.001

Aerobic P/PHA uptake*+ 1.900 Yield (aerobic, OHO) + 0.844

Propionate yield (fermentation, high H2)* 1.897 Max. spec. growth rate (PAO)* 0.785

Yield (fermentation, high H2, OHO) + 1.897 Max. spec. growth rate, P-limited 0.695

OHO low pH limit (anaerobic) 1.896 Aerobic/anoxic decay rate (PAO)* 0.684

VFA sequestration half sat. 1.896 P uptake half sat. 0.647

Aerobic/anoxic decay rate* 1.896 Substrate half sat. (PAO) 0.636

*these parameters are similar for the un-calibrated and calibrated model. +these parameters are similar with the ones identified by Liwarska-Bizukojc & Biernacki (2010) for each state

accordingly. In Table 10 it is observed that six parameters are similar for both models which also follow a slightly similar ranking pattern. Compared to the results of Liwarska-Bizukojc & Biernacki (2010), five out of 10 parameters are similar for the un-calibrated model and four out of 10 for the calibrated one. In a statistical manner, the similarity of the results between the un-calibrated and calibrated model is reaching at 60% in the present study, compared to the 90% in the study of Liwarska-Bizukojc &

34

Biernacki (2010). This difference may have occurred due to the following reasons: each project used different model configurations, loading characteristics, calibrated parameters and BioWin® versions. The increased uncertainty of this study should also be considered. Further assumptions would be risky to be made, since the BioWin® ASDM model is using some relatively new parameters and their understanding needs further investigation. The influential role of the model parameters associated with PAOs, emphasized in other studies, was confirmed for a third consecutive time in this study. More specifically, five out of 10 parameters with a high δjmsqr for the un-calibrated model and seven out of 10 for the calibrated model are associated with PAOs. The calibrated parameter YP/acetic is identified as the most sensitive parameter in both model states. Regarding a very important parameter in Monod kinetics, the half saturation constant for OHOs (Ks) which was also calibrated, it occurred to be sensitive for both models on the basis of Si,j. However, on the basis of δjmsqr it occurred in the 14th place for the calibrated model and in the 17th for the un-calibrated model. Moreover, another common parameter in ASMs, the YH which describes the yield coefficient for OHOs under aerobic conditions, was identified in the 5th place for the calibrated model and in the 15th for the un-calibrated model.

Considering the results of the sensitivity analyses and the comments above, it is not yet clear whether the un-calibrated or calibrated model results should be considered as more reliable. However, the calibrated model sensitivity analysis, indicated more sensitive parameters associated with PAOs (7 to 5) – which are of particular interest in this study –, including the significant YH in the top 10 ranking of δjmsqr and also resulted to a more sensitive KS. Summing up, the un-calibrated model sensitivity analysis proved to be useful and provided a general picture of the most influential and sensitive parameters. On the other hand, the results of the calibrated model analysis can be considered as more reliable.

5.2. Steady-state model

Steady-state model calibration 5.2.1.

Model calibration was needed due to discrepancies between the measured and the simulated effluent values in both steady and dynamic model simulations, using data from January 2016. The least possible parameters were calibrated since it was avoided to arbitrarily calibrate any values without evidence for justification of each change.

COD fractionation was not possible during this work therefore the influent wastewater parameters were held at the default BioWin® values apart from one. The parameter of Fus which describes the un-biodegradable soluble fraction of CODtot was calibrated in order to match the influent BOD7 value. The default value of Fus is 0.05 gCOD/gCODtot in BioWin® and the value assigned was 0.09; this decreased the amount of the biodegradable COD and consequently the concentration of the

35

influent BOD7 from 255 mg/L (calculated by BioWin®) to the desired 240 mg/L.

Regarding the kinetics, the substrate half saturation constant for OHOs (KS) was calibrated which describes the affinity of the biomass for substrate. This parameter impacts the residual soluble substrate concentration in the effluent (BioWin 5.0 Help Manual). The default BioWin® value is 5 mgCOD/L whereas the calibrated value was set at 11.5 mgCOD/L in order to match the measured effluent BOD7 concentration at the steady state simulation. An increased value of KS implies a decreased affinity of the biomass for substrate. This might be an effect arising from effluent water discharging from pulp or textile industries. Indeed, two large pulp and paper industries operate in Gävle (BillerudKorsnäs AB, Stora Enso AB) followed by other similar or textile industries. The affinity of substrates originating from such industries is usually lower than for typical municipal wastewater Liwarska-Bizukojc et al. (2008). Moreover, this could also be caused by low concentrations of readily biodegradable compounds or, as Carter and Latiiwell (1967) stated, it may also be affected by the low temperatures in the plant during the winter period.

Regarding effluent Ptot the model demonstrated a lower removal rate than the measured one. The desired Ptot at the effluent was achieved by calibrating YP/acetic in order to increase the concentration of the effluent Ptot. YP/acetic describes the amount of P released for one milligram of acetate sequestered in the form of PHA (BioWin 5.0 Help Manual). The default BioWin® value for YP/acetic is 0.51 mgP/mgCOD whereas the calibrated value was set at 0.495 mgP/mgCOD which is an only 3% reduction of the amount of P released. This change could imply a poor performance of the biological P removal due to cold weather at the plant during the winter period. The steady-state simulation results of the calibrated model using values of January 2016 are presented in Table 11 along with the measured effluent values and their 95 and 99% confidence intervals. A graphical representation is illustrated in Fig. 5.

Table 11. Measured effluent water variables with confidence bands and steady state simulation results of the calibrated model. January 2016

Confidence Interval 95%

Confidence Interval 99%

Simulated values

Parameter Unit n* Measured Mean

St. Deviation Min Max Min Max

COD mg O2 l-1 2 49 1.41 47.05 50.95 46.43 51.57 49.6

BOD7 mg O2 l-1 4 5 0.82 4.2 5.8 3.94 6.06 4.71

N mg N l-1 4 37.5 7.14 30.5 44.5 28.3 46.7 38.23

NH4 mg N l-1 31 33.52 4.93 31.78 35.26 31.24 35.8 35.15

P mg P l-1 31 0.265 0.05 0.248 0.282 0.243 0.287 0.26

PO4 mg P l-1 31 0.050 0.017 0.043 0.055 0.041 0.058 0.041

Outflow m3 d-1 4 30,660 4,659 29,019 32,300 28,504 32,815 30,913

*n indicates the number of samples that contribute to the measured mean values.

36

Figure 5. Simulated (bars) and measured values of the effluent water variables and their 95% confidence bands for the steady-state calibrated model.

As is evident in Table 11 and Figure 5, the steady state calibrated model successfully described the measured values of the effluent water variables. Although Ptot reached the upper threshold of the 95% confidence interval, PO4-P lies slightly out of the lower threshold for its value. However, PO4-P lies within the thresholds at the 99% confidence interval. These discrepancies in the results, especially regarding P and its fractions, might occur as a result of the elimination of the addition of chemicals in the plant. In more detail, when the value of the PO4-P fraction in the influent water is manually increased, its effluent value becomes acceptable and in contrast, the effluent value of Ptot lies above the upper threshold.

Steady-state model validation 5.2.2.

In order to validate the calibrated model it was examined if the model predictions and measured values from an independent dataset (March 2016) were within the acceptable tolerances. Similarly to the model calibration procedure, the model validation results were evaluated against the measured effluent values and their 95% and 99% confidence intervals (Table 12; Fig. 6).

COD [SERIES NAME]

[SERIES NAME] [SERIES NAME] [SERIES NAME]

[SERIES NAME] [SERIES NAME]

0

10

20

30

40

50

60

70

Effluent Variables

Conc

entr

atio

ns

37

Table 12. Measured effluent water variables with confidence bands and steady state simulation results for model validation.

March 2016

Confidence Interval 95%

Confidence Interval 99% Simulated

values Parameter Unit n*

Measured Mean

St. Deviation Min Max Min Max

COD mg O2 l-1 2 36 2.83 32.08 39.92 30.85 41.15 35.75

BOD7 mg O2 l-1 5 4.8 0.83 4.06 5.54 3.83 5.77 5.37

N mg N l-1 5 25.4 5.64 20.46 30.34 18.9 31.9 24.50

NH4 mg N l-1 31 23.79 4.39 22.25 25.35 21.77 25.83 22.41

P mg P l-1 31 0.314 0.11 0.274 0.353 0.261 0.353 0.293

PO4 mg P l-1 31 0.157 0.114 0.114 0.198 0.1 0.213 0.115

Outflow m3 d-1 4 42103 6906 39671 44534 38908 45297 40142

*n indicates the number of samples that contribute to the measured mean values.

Figure 6. Simulated (bars) and measured values of the effluent water variables and their 95% confidence bands for steady-state model validation.

5.3. Dynamic-state model

Dynamic-state model calibration 5.3.1.

The dynamic-state model simulation was conducted with no additional changes at the model parameters. Considering the increased degree of uncertainty regarding the available and created input data in the model, the simulation was considered satisfactory if the measured effluent values varied among ±20% of the simulated values. This assumption has been also practiced in previous works and offers an extra degree of freedom. The results for Ptot and BOD7 which are the two variables of main concern regarding the effluent concentrations are illustrated in Fig. 7 and 8 respectively.

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Figure 7. Dynamic-state simulation results of the calibrated model for the effluent Ptot using data of January 2016.

Figure 8. Dynamic-state simulation results of the calibrated model for the effluent BOD7 using data of January 2016.

It is evident that the calibrated model resulted in a satisfying description of the effluent Ptot. Generally, the simulated values follow the trends of changes of the measured values for both BOD7 and Ptot. Taking into account that the use of chemicals for P removal was not simulated, the fact that simulated Ptot values during some days lie above the measured ones can be considered as reasonable. More specifically, in the plant operation logbook it is reported that during the third and fourth week of

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January (18th-31st), 3 and 1 g/m3 of FeCl3 were used respectively and this can account for the lower measured values. Other reasons for discrepancies in the results might be errors in the assumed input values and the insufficient information available in the present work.

The main purpose in this study was to successfully predict the effluent BOD7 and Ptot. However, the model predictions for CODtot, Ntot, NH4-N, and PO4-P can also be considered satisfying and can be found in Appendix 5.

Dynamic-state model validation 5.3.2.

The model was validated against the dynamic data of March 2016. The results of the two variables of interest, Ptot and BOD7 are presented in Fig. 9 and 10 respectively.

Similarly to the dynamic-state calibrated model, the results of the dynamic-state model validation are considered acceptable when these follow the trends of changes of the measured values as also when there is not significant difference between the measured values and a range of ±20% of the simulated ones.

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Figure 10. Dynamic-state simulation results of the model validation for effluent BOD7 using data of March 2016.

Effluent Ptot demonstrates fairly satisfying results since it approaches an acceptable number of measured values and also it quite accurately follows the trends of the changes of those values. Simulated values present two extremes during the third and 16th-20th day of March respectively. In Table 13 it can be observed that there was an increased use of chemicals in March and especially during the 11th week (day 14-20). Considering

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Figure 9. Dynamic-state simulation results of model validation for effluent Ptot using data of March 2016 (The bottom chart presents the whole range of values whereas the top chart allows for a closer view of the lower values).

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that the use of chemicals was not modeled, this can account for the increased predicted values during that period. Moreover, the simulated values exhibit an increasing pattern at the end of the month during which, the use of FeCl3 is also increasing. Finally, it is also possible that these discrepancies occur partly due to errors in the created values.

Table 13. Use of chemicals (FeCl3) for P precipitation during March 2016. Week Dates FeCl3 (g/m3)

9 Feb 29 - Mar 6 5

10 Mar 7 - Mar 13 2

11 Mar 14 - Mar 20 16

12 Mar 21 - Mar 27 8

13 Mar 28 - Apr 3 31

Similarly to Ptot, the predictions of the model regarding effluent BOD7 values are quite satisfying since they approach the measured values and follow the trends of their changes.

As mentioned in the previous section, the main purpose of the model was to successfully describe the emissions of BOD7 and Ptot since these are the two variables controlled by a permit in the effluent of the plant. The model predictions regarding the variables of CODtot, Ntot, NH4-N, and PO4-P were not precise enough. However, the trends of changes of the simulated values for these variables follow the trends of the measured ones and therefore can be considered acceptable. The results can be found in Appendix 5.

5.4. Predictions of other significant processes of the WWTP It was considered necessary to evaluate the predictability of the model regarding other significant processes in the Duvbacken WWTP, as an additional form of evaluation and validation of the model. The processes evaluated were the average sludge production, the performance of the anaerobic sludge digester and the RAS fermenter. Information regarding other processes in the plant was not available and even for the ones evaluated, the amount of information was not sufficient.

Waste sludge production 5.4.1.

Data regarding the waste sludge production were available in the environmental reports of the plant in the form of total annual values. In Table 14 it can be observed that the produced sludge amount has a positive rate of change except for the year of 2013 that there was a reduction. The average annual sludge production of the last six years accounts for 1,300 tons total solids (TS) with a standard deviation of only 28, indicating a quite stable TS content.

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Table 14. Annual sludge production. Parameter Unit 2010 2011 2012 2013 2014 2015

Produced sludge amount

ton 6,590 6,690 6,521 6,668 6,003 5,829

%TS 19.3% 19.4% 20.5 % 19.0 % 21.7 % 22.8%

ton TS 1,272 1,298 1,337 1,267 1,303 1,329

It should be noted that the sludge dewatering process was not simulated which implies a predicted sludge volume larger than the measured one. Therefore the sludge production was evaluated based only on the TS content. BioWin® only predicts the TSS sludge content rather than the TS content. However, the measured sludge is a thickened, digested and dewatered sludge which implies that the content in dissolved solids would be very low compared to the suspended solids. This fact allowed the relation of measured TS to the predicted TSS content.

The estimation of the average sludge TSS content for 2016 was based on the predicted TSS from January and March of the same year. The steady-state simulation predicted a TSS content of 2,956 and 2,638 kg/d for January and March respectively. According to these values the annual TSS production for 2016 is estimated at 1,020 ton TSS. Assuming the smaller amount of TDS in the dewatered sludge which will add up to this amount, the model prediction appears quite satisfying when compared to the average measured 1,300 tons TS of the last six years.

Chemical precipitation 5.4.2.

First, a few important facts must be noted. During this work, there was absence of significant information regarding the accurate chemical dosing, points of application and water flow balance around the dewatering units. The dewatering process in turn, produces a strong side stream of wastewater rich in nutrients that returns to the plant. This high content of nutrients interacts with the whole system and in absence of chemicals’ simulation, the plant would not be possible to be modeled. As observed after attempts to simulate the addition of chemicals in the model and as also reported from BioWin® online support representative, chemical P removal systems are generally difficult to simulate and also solve. Therefore, the chemical addition was not simulated. In turn, it is important to mention that the exclusion of the dewatering process from the model and hence of the strong side stream was assumed to be balanced by the exclusion of the chemicals. However, it was attempted to roughly investigate if the model will respond correctly to a chemical addition along with the addition of the dewatering units in order to confirm its ability to approximate the performance of the real plant.

The annual amount of FeCl3 used for phosphorus precipitation is rather unsteady in the plant. For instance, 127.2 tons were used in 2013 while 344.4 tons were used in 2014. According to the aforementioned annual

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consumption it results that the dossing of FeCl3 ranges from 6.8 to 30 g/m3.

In the annual reports of the plant it is reported that Fecl3 addition is taking place on demand only in the secondary settler and the overflow tanks. On the other hand, in a copy of the plant’s scheme as appeared in the plant’s on-line management software, the chemical addition appears to take place in more than 3 points. However, based on the average values of the previous years, a FeCl3 dosage of 10 g/m3 (334 kg/d) was applied in the secondary settler. The effluent P was then much dependent on the flow specifications of the dewatering unit. After repeated flow adjustments at the dewatering unit, the target effluent P concentration was managed to be achieved with a constant chemical addition of 10 g/m3. However, data for confirming the validity of these flows was not available.

Sludge fermentation (Side stream hydrolysis) 5.4.3.

The performance of the RAS fermenter can be evaluated in terms of the amount of VFAs produced in it. BioWin® allows for the calculation of each process’s rates through a specific excel spreadsheet. The production and consumption rates (positive and negative values respectively) of acetate and propionate are presented in Table 15. Methanol presented neutral consumption or production; this fact can be considered as an indicator of a properly operating fermenter since methanol production is not desirable in this process.

Table 15. Produced and consumed quantities of acetate and propionate in the RAS fermenter. Process Acetate (g/d) Propionate (g/d)

Sequestration of acetate by PAO -60,335.86 0

Sequestration of propionate by PAO 0 -151,920.86

PHA release on ana. decay of PAO 7,437.82

Fermentation of SBSC by OHO (low H2) 9,510.215 0

Fermentation of SBSC by OHO (high H2) 43,476.68 152,168.38

During the simulation of January and March 2016 in this study the fermenter element in BioWin® presented almost a zero concentration of VFAs. This implies that all the VFAs produced were simultaneously consumed by PAOs in the fermenter as a result of increased Ptot concentrations, low rbCOD or both. WEF (2005) in their manual of practice no.29 reported that the ratio of VFA produced per VSS added to a fermenter has a broad range varying from 0.05 to 0.3 gVFA/gVSS. According to that, the VFA production rate at Duvbacken WWTP can be considered rather low. More specifically, the produced VFA/ addedVSS ratio occurs at around 0.077.

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Recent data in order to evaluate the performance of the fermenter was not available. Therefore, a rough evaluation was only possible with data available from the work of Kumpulainen (2013) who attempted to assess the performance of the sludge fermenter. The available data regard COD, VFA and PO4-P measurements at different sampling points, lasted from middle February 2013 to middle March 2013. Information about the average wastewater inflow and influent COD was acquired from the annual report of the plant for that year and was used as input to the model. The complete set of inputs to the model are presented in Table 16; the values of the rest input variables were similar as the ones used in the previous simulations. It has to be noted that the influent Ptot value was estimated based on the average sampled PO4-P values available for that period.

Table 16. Input variable values for the model simulation regarding the performance evaluation of the sludge fermenter.

Variable Value

Flow (m3/d) 34,388

Total COD (mgCOD/L) 540

Total Kjeldahl Nitrogen (mgN/L) 48

Total P (mgP/L) 6.4

The model simulation results are presented in Table 17. It can be observed that using the data of Kumpulainen (2013), the model predictions – especially for the fermenter – vary close to similar levels with the measured variables. A presence of available VFAs in the fermenter can be noticed, in contrast to this study, where no VFAs were available after the steady state simulation. This is a result of the low influent P concentrations in combination with the increased value of COD during the period that the sampling of Kumpulainen was conducted. Based on these results, it is confirmed that the absence of available VFAs in the fermenter during the simulations of the present study should not be considered as a simulation error rather than a result of the increased Ptot values of the influent wastewater.

Table 17. Results of model simulation using data from Kumpulainen (2013) for assessment of the performance of the sludge fermenter. PO4-P (mg/L) VFA (mg/L)

Influent Primary settler Anaerobic Aerobic1 Fermenter RAS Influent Fermenter

Kumpulainen(2013) 3.12 5.34 11.86 2.66 28.92 2.93 16.69 115

Simulated 3.1 3.47 9.26 1.12 27.29 9.3 15.84 146

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Anaerobic digester 5.4.4.

The available information for the two anaerobic digesters currently operating at the plant regarded only their design and receiving flow. Information acquired from the plant process engineer regard the average biogas flow at 164 m3/h with a 62% methane content during May 2016.

The simulation of the digester was not of much importance regarding this study. In addition, digesters are very difficult to model and usually their modeling is a subject of a separate study. Therefore, an approximate model prediction of the digesters performance was considered as satisfying for this study. The results of previous simulations using data of January and March 2016 presented values of biogas flow and methane content close to those reported by the process engineer of the plant. In more detail, the results can be reviewed in Table 18.

Table 18. Steady state results for the off-gas flow rate of the fermenter and its methane gas content. January March Measured

Off-gas flow (m3/h) 114.18 99.76 164

Methane (%) 64.1 64.4 62

5.5. Scenarios

Scenario 1: Plant emissions at 120,000 P.E. 5.5.1.The plant loading values for flow, BOD7, Ptot and Ntot at the time that the person equivalent will be at 120,000 P.E. were initially estimated (Appendix 3). Scenario 1 was simulated using the steady-state model since a long-term prediction was needed. Using a SRT of 7.5 days and the input data presented in Table 19, the model predicted the emissions demonstrated in Table 20. It has to be mentioned that the flow of 51,101 m3/d with a COD of 303 mg/L corresponds to 8,400 kgBOD7/d which is equal to the loading of 120,000 P.E.

Table 19. Input model values for the simulation of Scenario 1. Name Value

Flow (m3) 51,101

Total COD* (mgCOD/L) 303

Total Kjeldahl Nitrogen (mgN/L) 47.53

Total P (mgP/L) 7.78

Nitrate N (mgN/L) 0

pH 7.3

Alkalinity (mmol/L) 6

ISS Influent (mgISS/L) 15

Calcium (mg/L) 80

Magnesium (mg/L) 15

Dissolved O2 (mg/L) 0

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In Table 20 it is evident that the annual BOD7 emissions lie quite below the permit of 120 tons/yr whereas the Ptot emissions slightly exceed the permit of 6 tons/yr. However, the P emissions can be controlled by the addition of a larger amount of FeCl3. Regarding nitrogen, the predicted emissions are also increased which in case of a permit application will lead to an unavoidable upgrade of the current plant processes.

Table 20. Predicted plant emissions for Scenario 1. Elements mg/L kg/d tons/yr

CODtot 37.2 1,891 690

BOD7 4.94 251.3 91.7

Ntot 36.4 1,854 676

Ptot 0.36 18.1 6.60

PO4-P 0.037 1.86 0.68

Scenario 2: Plant emissions at 120,000 P.E. and maximum inflow since 5.5.2.2010

According to the measurements of the previous years, the maximum amount of wastewater that the plant received since 2010 was 170 m3/p,yr (0.465 m3/p,d). Using the similar influent loading values as for Scenario 1 and the inflow rate of 57,259 m3/d (0.465m3/p,d * 122,938 connected people at 120,000 P.E.) the model predicted the values presented in Table 21.

Table 21. Predicted plant emissions for Scenario 2. Elements mg/L kg/d tons/yr

CODtot 34.15 1,949 711

BOD7 5.04 287.6 105

Ntot 32.8 1,873 684

Ptot 0.36 20.4 7.45

PO4-P 0.039 2.22 0.81

It can be observed that the values of all effluent variables were increased. However, BOD7 still varied below the permit.

The model finally predicted that the BOD7 effluent permit can be surpassed at 120,000 P.E. with an influent flow rate of around 65,000 m3/d.

Scenarios outcome 5.5.3.

Based on the predictions of the model the population increase will not considerably affect the BOD7 removal. On the other hand Ptot removal

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exhibited a trend for surpassing the current effluent permit. This implies an increase in the use of chemicals or the need for improvement and upgrade of the P removal process. In addition, there is a balance limit for the amount of chemicals that can be used along with bio-P over which, the process is inhibited. If this limit is surpassed in the future, then the treatment method would probably have to change to full-scale chemical precipitation. Moreover, the introduction of a N permit will certainly lead to a retrofitting of the plant.

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6. FUTURE PROPOSALS

Future proposals regarding the operation of the plant under the current permits and in case of the introduction of nitrogen permit are presented.

6.1. Proposal without nitrogen permit According to the model predictions the performance of the plant under the current permits will be considerably efficient in the near future. However, some small-scale actions may be beneficial for the plant regarding effluent quality and saving costs.

Given the eutrophic conditions in the Inner and Outer Bay as also the population increase, the future permit of P may further decrease to less than 0.3 mg/L. A simple solution could be the increase of chemicals for further reduction of P emissions or even the switching to a full-scale chemical P removal with pre and post-precipitation steps. However, the introduction of the bio-P process reduced the use of chemicals by almost 85% which also implies a significant reduction in the produced amount of sludge and the routes of trucks for chemicals supply and sludge removal from the plant. Therefore, the removal of the bio-P process will lead to an equal size increase of the aforementioned operations. Moreover, a considerable increase in the use of chemicals could adversely affect the bio-P process since P would be bound on metals and hence, it will not be available for PAOs. Moreover, the presence of a large amount of metals bound on P in sludge would make it inappropriate to be spread in agricultural areas for soil fertilization and will make P extraction harder.

During May to August 2015, pilot runs of effluent water filtering with disc filters was evaluated in the plant. The process suggests the installation of disc filters in one of the six overflow tanks as a final polishing step which will operate along with the current bio-P and the occasional use of FeCl3. The results obtained from this process test regarding P removal were promising and therefore it can be proposed for full-scale implementation instead of switching to a complete chemical P removal process. However, it is uncertain whether the installation of disc filters can meet a potential P permit of 0.1 mg/L or operate efficiently in full-scale application and therefore this has to be further investigated.

6.2. Proposal with nitrogen permit Based on the eutrophic conditions that prevail in the Inner and Outer Bay, it is likely that a nitrogen permit will soon be applied. This in turn will demand the addition of additional processes in the plant.

Unlike P that is removed by sedimentation and filtration, nitrogen is transformed through nitrification – denitrification and removed by biological reactions. During nitrification, specific bacteria transform ammonia to nitrite and nitrate in an aerated environment and then, in an anoxic environment bacteria transform nitrate to nitrogen gas which

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leaves the plant. N removal combined with P removal can be practiced in two different configurations. First, by pre-denitrification where an anoxic process is placed between the anaerobic and aerobic processes. In that case, anaerobic tank’s effluent provides the carbon source needed for denitrification and nitrates are recirculated to the anoxic tank after they are nitrified in the downstream aerated tank. During post-denitrification, the anoxic process is placed after the aerobic one where it however demands the addition of an external carbon source and no recirculation.

Considering the aforementioned, if the operator decides to maintain the bio-P while N permit is applied, this implies a need for construction of additional process tanks and increase the footprint of the plant.

Alternatively, the transition to membrane bioreactors (MBR) is proposed. MBR is a relatively new technology which separates the water from the sludge within the aeration tanks and therefore, the secondary precipitation is not needed. Consequently, all the volume is free to accommodate a part of the nitrogen removal process, leading to a smaller increase demand of the plant’s footprint. MBRs are costly to operate due to the power consumption to pump water through membranes, higher aeration and cleaning costs. However, sludge production and water turbidity is decreased, pathogens are removed and a cleaner effluent water is produced.

Finally, an even smaller plant expansion would be needed if the operator proceeds with MBRs and full-scale chemical P removal which will eliminate the bio-P and secondary sedimentation.

6.3. Complementary proposals Some additional improvements regard the relief of the plant from high BOD and P loading.

Regarding BOD, chemical precipitation can be used in the primary settler in cases of peak loadings and consequently reduce downstream aeration costs and effluent concentrations. However, this action has to be balanced with the operation of bio-P in order to allow the supply of the adequate amount of organics and make sure that the chemicals will not negatively affect bio-P.

An increased P release is currently observed in the sludge fermenter. In order to relieve the system, P precipitation with calcium chloride was recently tested in the laboratory of the plant with promising results. It was predicted that a maximum 20% of the total P of the plant can be removed by this process.

An additional reduction of the organic and nutrient loading of the plant could be achieved by upstream pre-treatment at various point sources that contribute to the plant’s loading. However, this action would be difficult to implement since it demands private capital.

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7. CONCLUSIONS

Gävle community in Sweden expects an almost 20% increase in population by 2030 (120,000 people). Duvbacken WWTP was designed for 100,000 P.E. and is currently treating the largest portion of wastewater generated in the community. The plant is operating close to the design capacity and therefore the operator seeks for an extension at 120,000 P.E. In this work, a computer model of the Duvbacken WWTP was developed in BioWin® software under a significant lack of data, in order to predict the emissions of the plant at 120,000 P.E. and propose future solutions. Consequently, the results showed that a satisfying model can be developed even with limited available data. The model predicted a satisfying performance of the plant at 120,000 P.E. and also provides a tool for further development and improvement that could contribute to the control and fine tuning of the plant’s processes. Finally, the proposals for further development of the WWTP processes could help the operator meet the stringent permits in the future. The conclusions of this project are drawn in the following points:

• A general model that approximates the processes of a WWTP and predicts its behavior can be developed even with a lack of significant – in terms of modelling – data.

It is important to mention that in order to successfully apply such a complex model to a real process, an extensive measurement and experimental campaign is required to determine various modelling aspects, parameters and characterize the influent wastewater. In this study, the inadequate provided data were elaborated and finally resulted in comprehensive newly created datasets. Consequently, the new datasets were assumed to be realistic and the modeling process was carried out. However, solid conclusions should not be directly drawn since the aforementioned assumption introduced an increased degree of uncertainty to the whole modelling procedure. The results of the created model should therefore be carefully evaluated based on adequate knowledge and experience.

• Sensitivity analysis, as used in this work before the model calibration, proved to be a very helpful tool. Since there was no information about the characteristics of the plant it was impossible to identify which parameters should be calibrated in the model. The analysis was used on the un-calibrated model in order to indicate the most sensitive and influential parameters among which the modeler should identify the ones that need to be calibrated. This practice is effective especially when the model is using a large number of parameters with respect to the available data such as BioWin®.

The sensitivity analysis results in this study were evaluated against the results of previous studies where they exhibited adequate similarities and thus confirmed their correctness. More specifically, the phosphorus/acetate release ratio for PAOs (YP/acetic) and the substrate half saturation constant for OHOs (KS), the first and

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fourteenth most sensitive parameters respectively, were considered necessary to be calibrated. Finally, the model was calibrated and the best fit to the measured data was accomplished. Following the model’s validity was confirmed using an independent dataset which resulted in fairly acceptable results.

• According to the model’s predictions, Duvbacken WWTP will be efficiently treating the incoming loading at 120,000 P.E. More stringent permits along with the introduction of N permit in the future may be addressed with MBRs and a full scale chemical P removal.

The model predicts a normal operation of the plant at 120,000 P.E. if the average predicted incoming flow rates and concentrations of organics and nutrients prevail.

The increase of P loading could be managed by larger amounts of chemicals combined with disc filters as a final treatment process. However, an increasing amount of chemicals can in turn inhibit the bio-P process at a specific point and result to a change to complete chemical P removal.

The scenario of a flow rate increase beyond the average predicted values, indicated BOD7 emissions over the permit. In such cases, chemical precipitation in the primary settler was suggested in order to reduce any influent BOD7 peaks.

In case of the application of N permit there is no question whether the configuration of the plant should change. On the contrary, a question can be set regarding the continuation of the bio-P process. In the case that the bio-P is combined with N removal, there will be a demand for larger volumes to facilitate the nitrification and denitrification processes. A smaller demand for additional space can be achieved by turning the bio-P process to full-scale chemical P removal in order to provide more space for the N removal processes. An even smaller area demand can be achieved by installing MBRs, maintaining the bio-P or introducing a full-scale chemical P removal. The aforementioned proposals may be equally efficient but before coming to a final decision, they should also be assessed based on their construction and operational costs.

• Despite the limited operational and biochemical data the model can be used combined with expertise and experience in wastewater treatment processes in order to: 1. Predict the effluent concentrations and the trends of their

changes. 2. Simulate the short and long-term performance and behavior of

the plant. 3. Predict the results of changing the operational parameters such

as recycle ratios, waste sludge flow rate, dissolved oxygen concentrations, solids removal efficiency and fermented sludge volume.

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4. Assist to further develop and improve the model after the acquisition of the required data and apply it as an element of a real time control system of the plant.

• The model predictions can be considered trustworthy unless if a simulation with new datasets repeatedly fails to predict the measured values.

Summing up, the model of Duvbacken WWTP, although developed with a limited amount of data, can assist the consultants or the process engineers to get an overall view of the behavior of the plant under various desired process changes. An improved version of the model, developed after adequate data acquisition may prove to be a very useful tool for the fine tuning and the prediction of the short and long-term behavior of the plant or individual plant processes. In addition, the proposed solutions regarding the future operation of the plant can be further assessed and then implemented in order to meet possible upcoming stringent permits. Combined with expertise and experience in the wastewater treatment sector, the aforementioned conclusions can eventually help improve the environmental condition in the eutrophic Inner and Outer Bay, recover nutrient resources, save capital costs and minimize the environmental impact of the plant.

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8. FURTHER RECOMMENDATIONS

A chapter including further recommendations was considered necessary and intends to help overcome some of the difficulties experienced during this study and lead to an improved model development. This in turn will offer the operator or the consultant working on the plant, a powerful tool to test and investigate any process modification desired. Moreover, some recommendations regarding the improvement of the future plant performance are proposed.

8.1. Improvement of the model The recommendations further formulated do not regard the types of experiments that have to be performed – since this is out of the scope of this study – rather than the information that need to be available for an improvement of the model developed. This set of information regards:

• The fractions of influent COD which affect all the plant processes. In the case of BioWin®, Envirosim provides an “influent specifier” spreadsheet in MS Excel in which the influent characteristics of the wastewater can be imported along with information about effluent filtered COD, influent filtered COD, filtered-flocculated COD and acetate and it estimates the COD fractions which are used in the model. Additionally, a basic methodology for COD fractions estimation can be found in the work of Mhlanga & Brouckaert (2013).

• A more comprehensive data set of flow rates. Even though the dataset of the flowrates was one of the most comprehensive in this study, flow rates regarding digester effluent, the very important waste sludge and plant effluent were missing.

• Characterization of the influent wastewater regarding COD, TKN, PO4-P, Ptot, NO3-N, NH4-N, Ntot, TSS, pH and temperature. As the plant process engineer mentioned some of these variables are measured once every two weeks or as a weekly average. However this sampling frequency is inadequate for a proper model development. Therefore, long lasting measuring campaigns have to be planned.

• The model could be also improved if the dissolved oxygen, NO3-N, ML(V)SS, PO4-P, NH4-N, pH and temperature are also measured in each plant process and especially in the bioreactors and the side streams of thickeners and dewatering units.

• Sludge blanket height in the clarifiers, return and waste sludge characteristics, daily sludge production and sludge age.

• Precise time, amount and point of application of FeCl3 in order to model the use of chemicals.

• If hydraulic characterization is of importance, tracer tests must be performed to obtain information about the mixing characteristics.

• For a better settling model selection, settling experiments and sludge volume index (SVI) measurements have to be performed.

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• New PO4-P and VFA measurements in the influent and the effluent streams of the primary settler and the fermenter in order to re-evaluate and better describe the plant ability for VFA production.

The determination of all model kinetic and stoichiometric parameters is an expensive and time consuming process and therefore the default values are used whenever it is possible. However, performing a model calibration demands information about important kinetic and stoichiometric parameters of the plant. For the determination of these parameters laboratory experiments such as batch tests are recommended. Comprehensive methodology about these experimental practices can be found in the work of Liwarska-Bizukojc & Ledakowicz (2011) and Vanrolleghem et al. (2003).

8.2. Sampling frequency As was widely discussed in this work, a large percentage of the data shortage is caused by the insufficient amount of measurements in the effluent and especially the influent wastewater. Moreover, insufficient sampling can lead to misjudgment of the plant’s performance and miscalculation of the average incoming loads and effluent emissions. Therefore, a more frequent sampling plan or more on-line measurements are recommended for an adequate, more precise and confident assessment of the performance and influent-effluent characteristics of the plant.

8.3. Assessment of the use of chemicals Prior to a final decision regarding the potential removal of bio-P process and the introduction of a full-scale chemical P precipitation it is recommended that the use of chemicals is carefully assessed. More specifically, the overall benefits arising from a potential switching to a full-scale chemical P removal against the disadvantages caused by the chemicals in the recipient should be compared.

8.4. Performance evaluation of disc filters against full-scale chemical P removal

Based on the discussion regarding disc filters and full-scale chemicals application for P removal in section 6, more experiments on the disc filters are recommended as also a feasibility study that can assess which solution would be more beneficial in terms of costs and environmental impact. Alternatively, such process changes could be assessed with an improved model of the plant.

55

REFERENCES

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of parameter subsets. Water Research, 36(16), pp.4113–4127. Carter, O.G. & Latiiwell, D.J., 1967. Effects of temperature on orthophosphate absorption by

excised corn roots. Chai, Q., 2008. Modeling, estimation and control of biological wastewater treatment plants. Department of Environmental Quality of Michigan, 2011. Activated sludge process control. Dochain, D. & Vanrolleghem, P.A., 2001. Dynamical modelling and estimation in wastewater

treatment processes. EPA, 1997. Wastewater treatment manuals - primary, secondary and tertiary treatment. , p.131. Gävle kommun, 2016. www.gavle.se. Available at: http://www.gavle.se/Bygga-bo-

miljo/Kommunens-planarbete/Oversiktplaner/Gavle-2030/Var-ska-Gavle-vaxa/ Gray, N.F., 2004. Biology of wastewater treatment. Gujer, W. et al., 1995. The Activated Sludge Model No. 2: Biological phosphorus removal, London. Henze, M. et al., 2000. Activated sludge models ASM1, ASM2, ASM2d and ASM3, London. Henze, M. et al., 2002. Wastewater Treatment - Biological and Chemical Processes. Henze, M. & Comeau, Y., 2008. Wastewater Characterization. Biological Wastewater Treatment:

Principles Modelling and Design., pp.33–52. Hu, X. et al., 2014. Calibration and validation of an activated sludge model for a pilot-scale

anoxic/anaerobic/aerobic/post-anoxic process. , 15(9), pp.743–752. Jabari, P., Yuan, Q. & Oleszkiewicz, J.A., 2016. Potential of hydrolysis of particulate COD in

extended anaerobic conditions to enhance biological phosphorous removal. Biotechnology and Bioengineering, (May), p.10.

Jeanette A. Brown et al., 2005. Biological nutrient removal (BNR) operation in wastewater treatment plants, Manual of Practice (Book 29).

Jeppsson, U., 1996. Modelling aspects of wastewater treatment processes. Kumpulainen, E., 2013. Evaluation and optimization of the sidestream hydrolysis process at

Duvbacken wastewater treatment plant. Uppsala Universitetet. Liwarska-Bizukojc, E. et al., 2011. Calibration of a complex activated sludge model for the full-scale

wastewater treatment plant. Bioprocess and Biosystems Engineering, 34(6), pp.659–670. Liwarska-Bizukojc, E. et al., 2008. Effect of anionic and nonionic surfactants on the kinetics of the

aerobic heterotrophic biodegradation of organic matter in industrial wastewater. Water Research, 42, pp.923–930.

Liwarska-Bizukojc, E. et al., 2013. Improving the operation of the full scale wastewater treatment plant. , 39(1), p.13.

Liwarska-bizukojc, E. & Biernacki, R., 2010. Identification of the most sensitive parameters in the activated sludge model implemented in BioWin software. Bioresource Technology, 101(19), pp.7278–7285.

Liwarska-Bizukojc, E. & Ledakowicz, S., 2011. Determination of kinetic and stoichiometric parameters of activated sludge models. Environment Protection Engineering, 37(3), p.73.

Makinia, J., Rosenwinkel, K.H. & Spering, V., 2006. Comparison of two model concepts for simulation of nitrogen removal at a full scale biological nutrient removal pilot plant. Journal of

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Environmental Engineering, 132(4), pp.476–487. Melcer, H., 2004. Methods for wastewater characterization in activated sludge modelling. IWA

Publishing. Mhlanga, F.T. & Brouckaert, C.J., 2013. Characterisation of wastewater for modelling of

wastewater treatment plants receiving industrial effluent. Water SA, 39(3), pp.403–408. Morling, S., 1988. Phosphorus removal by means of chemical pre-precipitation, Experiences from

Sweden. Digest from a paper presented at F.R.I. Workshop January 1988, Vaedbaek, Denmark.

Nutt, D. et al., 2004. The advantages of having a biological wastewater treatment model from a water company perspective. , p.15.

Petersen, B., 2000. Calibration, identifiability and optimal experimental design of activated sludge models. , p.364.

Petersen, B. et al., 2003. Calibration of activated sludge models: A critical review of experimental designs. In Agathos, S.N., Reineke, W. (Eds.), Biotechnology for the Environment: Wastewater Treatment and Modelling. Waste Gas Handling.

Petersen, B. et al., 2002. Evaluation of an ASM1 model calibration procedure on a municipal-industrial wastewater treatment plant. , 3(August 2015), pp.15–38.

US EPA, 1987. The enhanced stream water quality models QUAL2E and QUAL2E-UNCAS. , p.204.

Vanrolleghem, P.A. et al., 2003. A comprehensive model calibration procedure for activated sludge models. WEFTEC, p.28.

Weijers, S.R. & Vanrolleghem, P.A., 1997. A procedure for selecting best identifiable parameters in calibrating activated sludge model NO.1 to full-scale plant data. IAWQ, 36(5), pp.69–79.

I

APPENDIX 1 – COMPARISON BETWEEN REQUIRED AND AVAILABLE DATA

In this Appendix, a visual representation of the required against the available datasets for sufficient modeling is presented.

Required data for wastewater treatment plant modeling (Petersen et al. 2002).

Design data

Reactor volumes

Pump flows

Aeration capacities

Operational data Flow rates

pH, aeration, temperatures

Hydraulic model Results of tracer tests

Settler model Settling velocities

Biological model

Influent concentrations (COD, BOD, TKN, Ntot, NH4, NO3, NO2, Ptot, PO4, SS and other)

Effluent concentrations (COD, BOD, TKN, Ntot, NH4, NO3, NO2, Ptot, PO4, SS and other)

Intremediate streams concentrations (COD, BOD, TKN, Ntot, NH4, NO3, NO2, Ptot, PO4, SS and other)

Sludge composition Mass, TSS, VSS, COD, Ntot, Ptot and other

Reaction Kinetics (e.g. growth and decay rates)

Reaction Stoichiometry (e.g. yields)

Available data in this study (comprehensive datasets are underlined; datasets not underlined indicate an incomplete dataset and crossed words indicate the unavailable datasets).

Design data

Reactor volumes

Pump flows

Aeration capacities

Operational data Flow rates

pH, aeration, temperatures

Hydraulic model Results of tracer tests

Settler model Settling velocities

Biological model

Influent concentrations (COD, BOD, TKN, Ntot, NH4, NO3, NO2, Ptot, PO4, SS and other)

Effluent concentrations (COD, BOD, TKN, Ntot, NH4, NO3, NO2, Ptot, PO4, SS and other)

Intremediate streams concentrations (COD, BOD, TKN, Ntot, NH4, NO3, NO2, Ptot, PO4, SS and other)

Sludge composition Mass, TSS, VSS, COD, Ntot, Ptot and other

Reaction Kinetics (e.g. growth and decay rates)

Reaction Stoichiometry (e.g. yields)

II

APPENDIX 2 – CREATION OF COMPLETE DATASETS

In this appendix the methodology followed for the creation of the new datasets for January and March 2016 that correspond to the measured values is described. “Measured” values refer to real measured values obtained by the Duvbaken WWTP operation logbook whereas created values refer to those values created, based on the measured ones, during the present project. The created datasets regard the influent values of COD, Ptot and Ntot and are presented in sections A, B and C below.

Measured and created influent values for COD, Ptot and TKN during January 2016. “Measured” values were derived from Duvbacken WWTP logs while “Created” values were created for this study.

Measured average Created

Created average Measured average

Created Created average

Created

Date COD (mg/L)

COD (kg/w)

COD (mg/L) COD (mg/L)

Ptot

(mg/L) Ptot (kg/w)

Ptot (mg/L) Ptot (mg/L)

TKN (mg/L)

01-01-16 479 14.77 43

02-01-16 457 13.90 42

03-01-16 566 18.24 54

04-01-16 501 15.63 50

05-01-16 544 17.37 52

06-01-16 610 19.98 58

07-01-16 523 16.50 54

08-01-16 501 15.63 52

09-01-16 479 14.77 52

10-01-16 501 15.63 55

11-01-16

510 100,624

414

510 16 3,157

12.16

16

46

12-01-16 414 12.16 48

13-01-16 479 14.77 52

14-01-16 479 14.77 52

15-01-16 588 19.11 60

16-01-16 523 16.50 56

17-01-16 675 22.59 45

18-01-16 349 9.55 48

19-01-16 392 11.29 48

20-01-16 414 12.16 50

21-01-16 370 10.42 48

22-01-16 414 12.16 51

23-01-16 457 13.90 52

III

Measured average Created

Created average Measured average

Created Created average

Created

Date COD (mg/L)

COD (kg/w)

COD (mg/L) COD (mg/L)

Ptot

(mg/L) Ptot (kg/w)

Ptot (mg/L) Ptot (mg/L)

TKN (mg/L)

24-01-16 436 13.03 54

25-01-16

330 89,108

327

330 8.8 2,376

8.68

8.8

45

26-01-16 349 9.55 47

27-01-16 392 11.29 47

28-01-16 327 8.68 37

29-01-16 392 11.29 38

30-01-16 327 8.68 34

31-01-16 197 3.46 27

Measured and created influent values for COD, Ptot and TKN during March 2016. “Measured” values were derived from Duvbacken WWTP logs while “Created” values were created for this study.

Measured average Created

Created average Measured average

Created Created average

Created

Date COD (mg/L)

COD (kg/w)

COD (mg/L) COD (mg/L)

Ptot (mg/L)

Ptot

(kg/w) Ptot (mg/L) Ptot (mg/L)

TKN (mg/L)

01-03-16 220 6.66 27

02-03-16 287 8.68 36

03-03-16 531 16.08 66

04-03-16 220 6.66 27

05-03-16 227 6.89 28

06-03-16 257 7.78 32

07-03-16

260 64,666

301

260 7.2 1,791

9.13

7.88

38

08-03-16 264 8.01 33

09-03-16 242 7.34 30

10-03-16 235 7.11 29

11-03-16 272 8.23 34

12-03-16 264 8.01 33

13-03-16 242 7.34 30

14-03-16 235 7.11 29

15-03-16 242 7.34 30

16-03-16 220 6.66 27

17-03-16 287 8.68 36

18-03-16 435 13.17 54

19-03-16 457 13.84 57

IV

Measured average Created

Created average Measured average

Created Created average

Created

Date COD (mg/L)

COD (kg/w)

COD (mg/L) COD (mg/L)

Ptot (mg/L)

Ptot

(kg/w) Ptot (mg/L) Ptot (mg/L)

TKN (mg/L)

20-03-16 338 10.25 42

21-03-16 301 9.13 38

22-03-16 212 6.44 27

23-03-16 279 8.46 35

24-03-16 279 8.46 35

25-03-16 279 8.46 35

26-03-16 249 7.56 31

27-03-16 331 10.03 41

28-03-16 338 10.25 42

29-03-16 331 10.03 41

30-03-16 383 11.60 48

31-03-16 449 13.62 56

01-04-16 361

02-04-16 390

03-04-16 442

04-04-16

370 129,070

412

370

05-04-16 331

06-04-16 412

07-04-16 412

08-04-16 405

09-04-16 316

10-04-16 301

Measured influent values for BOD7, Ntot and NH4-N. Date BOD7 (mg/L) BOD7 (kg/d) Ntot (mg/L) Ntot (kg/d) NH4-N (mg/L) NH4-N (kg/d)

20-01-16 240 6,493 50 1,353 39 1,055

03-02-16 150 5,642 59 2,219 47 1,768

01-03-16 - - 67 1,413 49 1,033

16-03-16 130 6,312 27 1,311 21 1,020

13-04-16 140 6,187 29 1,282 25 1,105

V

A. COD dataset

The method for the creation of the COD dataset in not based on a scientific method rather than on an arbitrary method which resulted in values that corresponded to the measured ones. It was assumed that if the created values are similar to the measured ones, for the time period that these are available, then the whole created dataset can be considered as realistic.

The measured COD concentrations were plotted against the measured effluent Ptot concentrations for the same period. Then the linear equation that describes the relationship between these sets of values was derived from MS Excel. Following, using all the measured effluent Ptot concentrations for January in the equation, day by day, the created COD values presented in Table 24 were derived. The averages of the created values for the same periods that the measured data were available are also denoted in order to show how the measured and created data match.

Following the same method, the COD values for March 2016 were created (Table 25).

B. Ptot dataset

For the creation of the influent Ptot values the same method as for COD in the above section A was followed by plotting the measured Ptot concentrations against the measured effluent Ptot.

Henze & Comeau (2008) classified the influent municipal wastewater in three categories; high, medium and low. The characterization “high” indicates a concentrated wastewater which represents cases with low wastewater consumption and/or infiltration. The characterization “low” indicates a diluted wastewater under high water consumption and/or infiltration. Based on this classification, the created COD:P ratio ranges around 33-34 which according to Henze & Comeau (2008) falls on the limits of the “low” to “medium” categories.

In order to create values for March, another approach was practiced, using the COD:P ratio reported by Henze & Comeau (2008). Considering snowmelt, the wastewater would be more diluted for March and so, a lower COD:P ratio (32) than the one characterizing the wastewater of January was used. The created values are presented in Table 25.

C. TKN dataset

TKN was equated with Ntot since no nitrate or nitrite were assumed to be present in the influent. The influent TKN values were calculated based on the COD:N ratio reported by (Henze & Comeau 2008) for “low” wastewater. First, using an arbitrary method again, the effluent NH4-N values were multiplied by the same value to create new Ntot values. The result was considered satisfying until the average monthly COD:N ratio reached at the value of 9 which characterizes the “low” to “medium” wastewater.

For the created values of March, the same procedure as for January was followed.

VI

APPENDIX 3 - ESTIMATION OF INPUT DATA FOR SCENARIOS

This Appendix includes the methodology followed for the estimation of the input data of the model for the time period when the person equivalent loading to the plant will be at 120,000 P.E. or else, when the plant will be receiving an average of 8,400 kg BOD/d (calculated by dividing P.E/70g BOD/p,d). The estimated data are presented in Table 27. Note that the starred (*) years 2010-2015 are those for which there were available data on which the estimations were based. Following, the calculations performed are presented:

• The future population increase of the community was estimated based on the average rate of population increase from 2009 until 2015 which was 0.754% per year.

• Relating the amount of connected people to the amount of the total population of Gävle it was estimated that 87.1% of the total population is connected to the plant.

• Data about the annual total inflow to the plant were available for a time period from 2010 until 2015. As is reasonable the annual inflow does not strongly depend on the number of people contributing to the plant loading but also on the amount of precipitation, snowfall and infiltration in the sewer catchment. The total water amount corresponding to each person varied from 138 to 170 m3/yr. The average value of 153 m3/p, yr was considered in order to estimate the future receiving flow to the plant.

• The estimations of the previous years (2010-2015) suggest that an average of 68.4 gBOD7/d equals to each person connected. The values ranged from 56 to 78 g BOD7/p,d throughout these years which indicates an unstable production. Based on the average value, a rough estimation of the daily BOD7 loading to the plant was possible by relating this value to the estimated number of connected people. According to Henze et al. (2002) the daily BOD5 production per person varies between 15 and 80 g which equals to around 17.25 and 92 gBOD7/p,d. Henze et al. (2002) also reviewed different studies and summarized the average annual BOD5 production per person for various countries. Regarding Sweden they report a range of 25-30 kgBOD5/p,yr which equals to around 78 – 94 gBOD7/p,d. Compared to that range, the BOD7 loading to the plant can be considered rather low for the Swedish standards.

• The value of Ptot that equals to each person connected was also calculated based on the existing data. An average value of 3.24 gP/p,d was calculated with the lowest and highest values at 2.02 and 5.23 gP/p,d. Henze et al. (2002) suggest a range of 1-3 gP/p,d which implies that the average loading value of the Duvbacken plant to be considered as high. However, it has to be noticed that specifically for Sweden, they reported an annual production of 0.8-1.2 kgP/p,yr which accounts for 2.19 – 3.28 gP/p, d.

• Finally, regarding Ntot, an average of 19.8 gN/p,d was estimated with the values between 2010-2015 ranging from 12.4 to 27.3 gN/p,d.

Estimated values of future population and loading conditions (starred years represent years for which the values were available).

Year Community population

Connected people

Annual inflow (m3/yr)

Average inflow (m3/d)

Average BOD7 (kgBOD/d)

P.E. (based on BOD7)

Ptot (kg/d)

Ntot (kg/d)

2009* 94,352 - - - - - - -

2010* 95,055 86,000 14,027,887 38,433 6,193 88,469 178 1,104

2011* 95,428 82,700 12,813,325 35,105 6,453 92,181 167 1,024

2012* 96,170 83,157 14,118,309 38,680 5,452 77,677 435 2,274

VII

Year Community population

Connected people

Annual inflow (m3/yr)

Average inflow (m3/d)

Average BOD7 (kgBOD/d)

P.E. (based on BOD7)

Ptot (kg/d)

Ntot (kg/d)

2013* 97,236 84,242 12,944,169 35,463 6,524 93,198 260 1,693

2014* 98,314 84,644 11,650,493 31,919 5,137 78,640 260 1,910

2015* 98,877 85,450 12,091,869 33,128 4,827 68,958 339 1,992

2016 99,619 86,798 13,168,835 36,079 5,934 84,777 281 1,715

2017 101,074 88,066 13,361,174 36,606 6,021 86,015 285 1,740

2018 102,529 89,334 13,553,514 37,133 6,108 87,253 290 1,765

2019 103,984 90,601 13,745,853 37,660 6,194 88,492 294 1,790

2020 105,439 91,869 13,938,192 38,187 6,281 89,730 298 1,815

2021 106,894 93,137 14,130,532 38,714 6,368 90,968 302 1,840

2022 108,349 94,404 14,322,871 39,241 6,454 92,206 306 1,865

2023 109,804 95,672 14,515,210 39,768 6,541 93,444 310 1,890

2024 111,259 96,940 14,707,550 40,295 6,628 94,683 314 1,915

2025 112,714 98,208 14,899,889 40,822 6,714 95,921 318 1,940

2026 114,169 99,475 15,092,228 41,349 6,801 97,159 322 1,965

2027 115,624 100,743 15,284,568 41,876 6,888 98,397 327 1,990

2028 117,079 102,011 15,476,907 42,402 6,974 99,636 331 2,015

2029 118,534 103,279 15,669,247 42,929 7,061 100,874 335 2,040

2030 120,000 104,556 15,863,040 43,460 7,148 102,121 339 2,066

2031 121,455 105,824 16,055,379 43,987 7,235 103,360 343 2,091

2032 122,910 107,091 16,247,719 44,514 7,322 104,598 347 2,116

2033 124,365 108,359 16,440,058 45,041 7,409 105,836 351 2,141

2034 125,820 109,627 16,632,397 45,568 7,495 107,074 355 2,166

2035 127,275 110,895 16,824,737 46,095 7,582 108,312 359 2,191

2036 128,730 112,162 17,017,076 46,622 7,669 109,551 364 2,216

2037 130,185 113,430 17,209,416 47,149 7,755 110,789 368 2,241

2038 131,640 114,698 17,401,755 47,676 7,842 112,027 372 2,266

2039 133,095 115,966 17,594,094 48,203 7,929 113,265 376 2,291

2040 134,550 117,233 17,786,434 48,730 8,015 114,504 380 2,316

2041 136,005 118,501 17,978,773 49,257 8,102 115,742 384 2,341

2042 137,460 119,769 18,171,112 49,784 8,189 116,980 388 2,366

2043 138,915 121,037 18,363,452 50,311 8,275 118,218 392 2,391

2044 140,370 122,304 18,555,791 50,838 8,362 119,456 396 2,416

Mid 2044 141,098 122,938 18,651,961 51,101 8,405 120,076 398 2,429

2045 141,825 123,572 18,748,130 51,365 8,449 120,695 400 2,441

2046 143,280 124,840 18,940,470 51,892 8,535 121,933 405 2,466

VIII

Year Community population

Connected people

Annual inflow (m3/yr)

Average inflow (m3/d)

Average BOD7 (kgBOD/d)

P.E. (based on BOD7)

Ptot (kg/d)

Ntot (kg/d)

2047 144,735 126,108 19,132,809 52,419 8,622 123,171 409 2,491

2048 146,190 127,375 19,325,148 52,946 8,709 124,409 413 2,516

2049 147,645 128,643 19,517,488 53,473 8,795 125,648 417 2,541

2050 149,100 129,911 19,709,827 54,000 8,882 126,886 421 2,567

IX

APPENDIX 4 – BIOWIN® PARAMETERS MENTIONED IN THIS WORK

Process name Description

Hydrolysis rate (Common) Rate constant for hydrolysis of slowly degradable organics into readily degradable substrate.

Anaerobic hydrolysis factor (AD) (Common) Rate reduction factor for hydrolysis under anaerobic conditions in anaerobic digestion.

Max. spec. growth rate (AOB)

Determines the maximum specific growth rate of ammonia oxidizing biomass. Substrate and nutrient limitations will decrease the growth rate. This parameter has a direct impact on the nitrification capacity.

Max. spec. growth rate (OHO)

Determines the maximum specific growth rate of ordinary heterotrophs. Substrate and nutrient limitations will decrease the growth rate. This parameter is sensitive only in very high loaded plants (short SRT), and determines maximum BOD removal capacity.

Substrate half sat. (Ks) (OHO) This parameter impacts the residual soluble substrate concentration in the effluent. The value is usually low in normal municipal plants.

Aerobic decay rate (OHO) Decay rate constant under aerobic conditions. This parameter impacts the endogenous respiration rate and VSS destruction during aerobic stabilization.

Fermentation half sat. (OHO) Half saturation of complex substrate under anaerobic conditions

Yield (Aerobic) (OHO)

Amount of biomass COD produced using one unit of readily biodegradable complex substrate COD. The remaining COD is oxidized. This parameter is very stable in municipal plants and seldom needs adjustment. In case there is a mismatch between measured and simulated sludge production and OUR, try adjusting the influent fup (unbiodegradable particulate COD fraction) parameter or check wastage and SRT.

Yield (fermentation low H2) (OHO) Amount of biomass produced on one unit of complex substrate fermented, under low H2 concentration.

Yield (fermentation high H2) (OHO) Amount of biomass produced on one unit of complex substrate fermented, under high H2 concentration.

H2 yield (fermentation low H2) (OHO) Amount of hydrogen produced on one unit of complex substrate fermented, under low H2 concentration.

Propionate yield (fermentation high H2) (OHO) Amount of propionate produced on one unit of complex substrate fermented, under high H2 concentration.

Yield (anoxic) (OHO) Biomass yield on readily biodegradable complex substrate COD under anoxic conditions.

X

Process name Description

P in Biomass (OHO) P content of heterotrophs. This parameter influences the P removal in non bio-P systems, and the P content of the sludge.

Max. spec. growth rate (PAO) Determines the maximum attainable growth rate of phosphorus accumulating heterotrophic organisms if no substrate, DO or P limitation occurs.

Max. spec. growth rate, P-limited (PAO) Determines the maximum attainable growth rate of phosphorus accumulating heterotrophic organisms under phosphorus limiting conditions.

Substrate half sat. (PAO) Half saturation constant for PHA, used as substrate by phosphorus accumulating organisms.

Substrate half sat., P-limited (PAO) Half saturation constant for PHA, under phosphorus limiting conditions.

Sequestration rate (PAO) Rate constant for VFA sequestration to form PHA (stored substrate).

Cation half sat. (PAO) Half saturation constant for cation (primarily potassium) storage during poly-P synthesis.

Aerobic/anoxic decay rate (PAO) Decay rate constant under aerobic or anoxic conditions.

Anaerobic decay rate (PAO) Decay rate constant under anaerobic conditions.

Aerobic P/PHA uptake (PAO) Amount of P stored per unit of PHA oxidized in aerobic conditions

Yield of PHA on sequestration (PAO) Amount of PHA stored when 1 mg of acetate or propionate is sequestered.

P/Ac release ratio (YP/acetic) (PAO) Amount of P released for one mg of acetate sequestered in the form of PHA

Yield of low PP (PAO) Fraction of P stored in releasable poly-P form (rest of P is stored in high molecular weight, non-releasable poly-P)

OHO low pH limit (anaerobic) (pH) At a pH equal to this value the growth rate of ordinary heterotrophic biomass under anaerobic conditions will be reduced by 50%.

Aerobic/anoxic DO half sat. (Switches) This constant is used to switch off aerobic PAO activity under low DO conditions (that is in anaerobic and anoxic reactors).

PolyP half sat. (Switches) This constant stops sequestration of VFA and P release as the ratio of low molecular weight polyphosphate to PAO falls.

VFA sequestration half sat. (Switches) This is the half saturation concentration for the sequestration of acetate and propionate.

P uptake half sat. (Switches)

This constant stops growth with polyphosphate storage at low soluble phosphate concentrations. This constant will have an impact on the effluent soluble P concentration in a bio-P system.

XI

APPENDIX 5 – DYNAMIC MODEL CALIBRATION AND VALIDATION RESULTS FOR CODTO T, NTO T, NH4-N AND PO4-P

Calibrated model results (Using data from January 2016)

Calibrated dynamic model results for CODtot.

Calibrated dynamic model results for Ntot.

15

25

35

45

55

65

75

85

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Conc

entr

atio

n CO

D tot

(mg/

L)

Time (days)

Effluent CODtot

Measured COD Simulated COD

15

20

25

30

35

40

45

50

55

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Conc

entr

atio

n N

tot (

mg/

L)

Time (days)

Effluent Ntot

Measured N Simulated N +/-20%

XII

Calibrated dynamic model results for NH4-N.

Calibrated dynamic model results for PO4-P.

15

20

25

30

35

40

45

50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31Efflu

ent c

once

ntra

tion

NH 4

-N

Time (days)

Effluent NH4-N

Measured NH4-N Simulated NH4-N +/-20%

0

0,03

0,06

0,09

0,12

0,15

0,18

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Efflu

ent c

once

ntra

tion

PO4-

P (m

g/L)

Time (days)

Effluent PO4-P

Measured PO4-P Simulated PO4-P +/-20%

XIII

Validated model results (Using data from March 2016)

Validated dynamic model results for CODtot.

Validated dynamic model results for Ntot..

2025303540455055606570

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Efflu

ent c

once

ntra

tion

COD t

ot

Time (days)

Effluent CODtot

Simulated COD Measured COD +/-20%

12,00000

17,00000

22,00000

27,00000

32,00000

37,00000

42,00000

47,00000

52,00000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Efflu

ent c

once

ntra

tion

Nto

t (m

g/L)

Time (days)

Effluent Ntot

Simulated Ntot Measured Ntot +/-20%

XIV

Validated dynamic model results for NH4-N.

Validated dynamic model results for PO4-P.

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30 35Efflu

ent c

once

ntra

tion

NH 4

-N (m

g/L)

Time (days)

Effluent NH4-N

Simulated NH4-N Measured NH4-N +/-20%

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 5 10 15 20 25 30 35

Efflu

ent c

once

ntra

tion

PO4-

P

Time (days)

Effluent PO4-P

Simulated PO4-P Measured PO4-P +/-20% Series4

TRITA LWR Degree Project

ISSN 1651-064X

LWR-EX-2017:01

www.kth.se