Snip, Laura; Plósz, Benedek G.; Flores Alsina, Xavier; Jeppsson, Ulf A. C.; Gernaey, Krist - PDF

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Downloaded from orbit.dtu.dk on: Mar 13, 2017 Upgrading the Benchmark Simulation Model Framework with emerging challenges - A study of N2O emissions and the fate of pharmaceuticals in urban wastewater systems Snip, Laura; Plósz, Benedek G.; Flores Alsina, Xavier; Jeppsson, Ulf A. C.; Gernaey, Krist Publication date: 2015 Document Version Final published version Link to publication Citation (APA): Snip, L., Plósz, B. G., Flores Alsina, X., Jeppsson, U. A. C., & Gernaey, K. (2015). Upgrading the Benchmark Simulation Model Framework with emerging challenges - A study of N2O emissions and the fate of pharmaceuticals in urban wastewater systems. Kgs. Lyngby: Technical University of Denmark. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Upg r a din g theb en chm a r ksim u la tion Modelfr a m ewor kwithem er g in g cha lenges Astudyo 2 teof pharmaceuticalsinurbanwastewater systems LauraSnip P h.dt hesi s Ap r i l,2015 Upgrading the Benchmark Simulation Model Framework with emerging challenges A study of N 2 O emissions and the fate of pharmaceuticals in urban wastewater systems PhD Thesis Laura Snip April, 2015 CAPEC PROCESS Research Center Department of Chemical & Biochemical Engineering Technical University of Denmark i Preface This thesis is submitted in partial fulfilment of the requirements for acquiring a PhD degree at the Technical University of Denmark (DTU). The research has been carried out from May 2012 to April 2015 and has received funding from the People Program (Marie Curie Actions) of the European Union s Seventh Framework Programme FP7/ under REA agreement This thesis was prepared at the Center for Process Engineering and Technology (PROCESS) at the Department of Chemical and Biochemical Engineering under the main supervision of Professor Krist V. Gernaey and co supervision of Associate Professor Ulf Jeppsson at the Division of Industrial Electrical Engineering and Automation (IEA) at Lund University, Marie Curie Research Fellow Xavier Flores Alsina (PROCESS), Associate Professor Benedek Plósz at the Department of Environmental Engineering and Associate Professor Ulrich Krühne (PROCESS). In addition, there has been one research stay at Aquafin, Aartselaar, Belgium and another at Catalan Institute for Water Research (ICRA), Girona, Spain. This work could not have been possible and as pleasant as it was without my supervisors: Krist, thanks for the trust in me and all the freedom it came with. I enjoyed our meetings with jokes in Dutch/English/Danish; Ulf, thank you for the fruitful discussions, code checking, reference checking and hosting of dinners; Xavi, thank you a million times for not only getting me this PhD job and helping me with everything related to it whenever I needed it, but also for the nice apartment and the fun!; Benedek, thank you for sharing your knowledge on micropollutants and the fresh view; Ulli, thanks for wanting to be on the supervisor list. In addition, I want to thank everyone at Aquafin and ICRA for making my stay enjoyable. Lluís and Ignasi (2), it was a pleasure working with you! Riccardo, thanks for being a smart and hardworking Msc student. I would also like to thank Erik for helping me in stressful times. Of course, I didn t only work during the past three years; I managed to make me some friends! A big hug for my SANITAS family, making conferences and training weeks not feel as work but a lot of fun! A special thanks for Marina and Antonia as well as for my Benchmark buddies, Kim and Ramesh. I m happy I could share it all with you, ups and downs. I also felt at home in DK due to the nice CAPEC PROCESS people. Beginning your PhD with the country seminar coming up is a really good start, even if you get called a direct person! Of course, I cannot forget my family and friends back in the Netherlands (or Switzerland) for their support and visits with paprika ribbels chips. Both the visit and the chips were highly appreciated ;). Last but not least, thanks to Kees for all his support and encouragement, as well as the cooking, to make the most out of these three years, even if it meant spending time apart, then and now. Laura van Duijvendijk Snip Copenhagen, April 2015 ii This thesis reflects only the author s views and the European Union is not liable for any use that may be made of the information contained therein. iii Did you ever stop to think, and forget to start again? A.A. Milne iv v Abstract Nowadays a wastewater treatment plant (WWTP) is not only expected to remove traditional pollutants from the wastewater; other emerging challenges have arisen as well. A WWTP is now, among other things, expected to also minimise its carbon footprint and deal with micropollutants. Optimising the performance of a WWTP can be done with mathematical models that can be used in simulation studies. The Benchmark Simulation Model (BSM) framework was developed to compare objectively different operational/control strategies. As different operational strategies of a WWTP will most likely have an effect on the greenhouse gas (GHG) emissions and the removal rate of micropollutants (MPs), modelling these processes for dynamic simulations and evaluation seems to be a promising tool for optimisation of a WWTP. Therefore, in this thesis the BSM is upgraded with processes describing GHG emissions and MPs removal. Regarding GHGs emissions, the focus is placed on the production of nitrous oxide (N 2 O). As micropollutants comprise a wide range of chemicals, pharmaceuticals are selected here as specific examples to be studied. Different nitrification models containing N 2 O producing processes are tested and used for an extension of the BSM. Various challenges were encountered regarding the mathematical structure and the parameter values when expanding the BSM. The N 2 O models produced different results due to the assumptions on which they are based. In addition, ph and inorganic carbon concentrations have been demonstrated to significantly influence the nitrification. Therefore a physicochemical model in combination with a N 2 O model is calibrated with data from a full scale sequencing batch reactor (SBR) to gain insight into the N 2 O production pathways. Most likely the pathways of nitrifier denitrification and hydroxylamine oxidation alternated during the nitrification phase in the SBR. The BSM framework is also extended with the occurrence, transport and fate of pharmaceuticals. The occurrence is modelled with a phenomenological approach for pharmaceuticals, including a daily pattern and a stochastic approach for pharmaceuticals with a more random occurrence. Different sewer conditions demonstrated effects on the occurrence of the pharmaceuticals as influent patterns at the inlet of the WWTP were smoothed or delayed. The fate in the WWTP showed that operational conditions can influence the biotransformation, retransformation and sorption rates. In addition, inhibition and co metabolic effects can have opposite effects on the removal rates. A phenomenological influent generator has been successfully calibrated with high frequency data for traditional variables and data on the occurrence of pharmaceuticals and metabolites. The excretion pathways as well as in sewer transformation processes proved to be of importance when calibrating the daily patterns. Upgrading the BSM framework with these calibrated models can help to optimise the performance of a WWTP by not only taking operational costs and effluent quality into account, but also by including the GHG emissions and removal rates of pharmaceuticals. vi vii Resumé på dansk I dag forventes der at et spildevandsrensningsanlæg ikke kun fjerner de almindelige forurenende stoffer fra spildevandet. Indenfor de seneste årtier er mange nye udfordringer opstået, hvilket har markant øget kravene til rensningsanlæggene. For eksempel forventes et rensningsanlæg nu til dags også at minimere sit CO 2 aftryk (carbon footprint) og fjerne mikroforureninger fra spildevandet. Optimering af driften af et rensningsanlæg kan undersøges og forbedres ved brug af matematiske modeller, der kan anvendes i simuleringsstudier. Benchmark Simulation Model (BSM) blev specielt udviklet til objektiv sammenligning af forskellige drifts og reguleringsstrategier gennem simuleringer. Forskellige driftsstrategier på et rensningsanlæg vil sandsynligvis have en effekt på både udledningen af drivhusgasser og fjernelsen af mikroforureninger. Derfor var det oplagt at udvikle og bruge matematiske modeller af disse processer i dynamiske simuleringsstudier, med formålet at optimere driften af et rensningsanlæg. Det vil sige at mindske udledning af drivhusgasser og optimere fjernelse af mikroforureninger. Derfor er BSM platformen i denne afhandling blevet opgraderet med processer der beskriver dannelse og udledning af drivhusgasser, samt fjernelse af mikroforureninger. Vedrørende udledning af drivhusgas har afhandlingen fokus på produktion af lattergas (N 2 O). Mikroforureninger omfatter en lang række kemikalier, og her er lægemidler blevet udvalgt som specifikke eksempler til at blive undersøgt nærmere. Flere nitrifikationsmodeller indeholder processer til dannelse af N 2 O og kan derfor anvendes til en udvidelse af BSM platformen. I forbindelse med denne udvidelse opstod flere udfordringer relateret til strukturen af den matematiske model til at beskrive N 2 O dannelse, samt til parameterværdierne der skal bruges når BSM platformen udvides. De kendte N 2 O modeller producerer forskellige resultater på grund af forskel i de forudsætninger, som modellerne er baseret på. Desuden har ph og uorganisk kulstof vist sig at have en betydelig indflydelse på nitrifikationsprocesserne. Derfor blev en fysisk kemisk model kombineret med en N 2 O nitrifikationsmodel kalibreret på basis af et datasæt, fra en fuldskala sekventielt batch reaktor (SBR), til at få bedre indsigt i N 2 O produktionsmekanismerne. Konklusionen er at det er sandsynligt at nitrificerendebakterier skifter mellem denitrifikation og hydroxylamin oxidation til N 2 O dannelse under nitrifikationsfasen i SBR eksemplet. BSM platformen blev også udvidet med modeller til at beskrive forekomsten, transport og skæbnen af lægemidler i spildevand. Forekomsten er blevet modelleret med en fænomenologiskmodel der er specielt udviklet til farmaceutiske produkter. Forekomsten beskrives på basis af et dagligt mønster, eller en stokastisk tilgang specifikt for lægemidler med en mere uregelmæssig forekomst. Ændringer i betingelserne i kloaksystemet har vist sig at have en indflydelse på forekomsten af lægemidler, ved enten at forsinke eller udligne forekomsten af lægemidler i indløbet til rensningsanlægget. I rensningsanlægget kan de viii operationelle forhold påvirke balancen mellem biotransformation, re transformation og sorption af lægemidler. Desuden kan hæmning og co metabolisme have den modsatte effekt på mængden af lægemidler der fjernes i anlægget. En fænomenologiskmodel til beskrivelse af lægemidler i indløbet er blevet kalibreret med højfrekvente data for de traditionelle variable og specifikke data om forekomsten af lægemidler og metabolitter. Hvordan lægemidler udskilles samt transformationsprocesser i kloakken viste sig at være af stor betydning ved kalibrering af de daglige mønstre. Opgradering af BSM platformen med disse kalibrerede modeller kan hjælpe med at optimere ydeevnen for et rensningsanlæg ved ikke blot at tage driftsomkostninger og spildevandskvalitet i betragtning, men også ved at inddrage udledningen af drivhusgas og fjernelse af lægemidler som kriterier til at beskrive effektiviteten af rensningsprocesserne. ix Nomenclature Abbreviations ADM1 Anaerobic Digestion Model No.1 AER Aerobic tank in the activated sludge unit of the BSM1 ANOX Anoxic tank in the activated sludge unit of the BSM1 AOB Ammonia oxidising bacteria AS Activated sludge ASMs Activated Sludge Models ASM1 Activated Sludge Model No. 1 ASM2 Activated Sludge Model No. 2 ASM2d Activated Sludge Model No. 2d ASM3 Activated Sludge Model No. 3 ASMN Activated Sludge Model for Nitrogen ASM X Activated Sludge Modelling Framework for Xenobiotic Trace Chemicals BCM Biochemical model BOD 5 Biochemical oxygen demand [g O 2 /m 3 ] BSM1 Benchmark Simulation Model No.1 BSM2 Benchmark Simulation Model No.2 CH 4 Methane CIP Ciprofloxacin, antibiotic drug CMZ Carbamazepine, anti epileptic drug CMZ 2OH Metabolite of carbamazepine, 2 Hydroxy Carbamazepine CO 2 Carbon dioxide CO 2 e CO 2 equivalent COD Chemical Oxygen Demand [g COD/m 3 ] CSTR Continuously stirred tank reactor DCF Diclofenac, non steroidal anti inflammatory drug GHG Greenhouse gas GWP Global Warming Potential [kg CO 2 e/100 years] HH Households block in influent generator HRT Hydraulic retention time [h] IBU Ibuprofen, non steroidal anti inflammatory compound IBU 2OH Metabolite of ibuprofen, 2 Hydroxyibuprofen IC Inorganic carbon IndS Industry block in influent generator IoAd Index of Agreement (Quantitative evaluation criteria) IWA International Water Association MAE Mean absolute error (Quantitative absolute evaluation criteria) x MARE Mean absolute relative error (Quantitative relative evaluation criteria) ME Mean error (Quantitative absolute evaluation criteria) MLSS Mixed Liquor Suspended Solids [g/m 3 ] MLVSS Mixed Liquor Volatile Suspended Solids [g/m 3 ] MP Micropollutant MPE Mean percentage error (Quantitative peak evaluation criteria) MSDE Mean squared derivative error (Quantitative peak evaluation criteria) MSRE Mean squared relative error (Quantitative relative evaluation criteria) N 2 Dinitrogen gas NH 2 OH Hydroxylamine NO 2 Nitrite NO 3 Nitrate NOB Nitrite oxidising bacteria O 2 Oxygen OCI Operational cost index [cost unit/d] OHO Ordinary heterotrophic organisms PCM Physicochemical model PDIFF Peak difference (Quantitative peak evaluation criteria) PE Person equivalent ( Households and Micropollutants model block) [ ] PEC Predicted environmental concentration [g/l] PEP Peak error percentage (Quantitative peak evaluation criteria) PNEC Predicted no effect concentration [g/l] QQ plot Quantile quantile plot (Qualitative evaluation criteria) RMSE Root mean square error (Quantitative absolute evaluation criteria) RWQM1 River Water Quality Model No.1 SBR Sequencing batch reactor SMX Sulfamethoxazole, antibiotic drug SMX N4 Metabolite of sulfamethoxazole, N4 acetyl sulfamethoxazole SRT Sludge retention time [d] STD Boots Standard deviation of the Bootstrap calibration T Temperature [ C] TCY Tetracycline, antibiotic drug TN Total nitrogen [g N/m 3 ] TP Total phosphorus [g P/m 3 ] TSS Total suspended solids concentration [g S S /m 3 ] WFD EU Water Framework Directive WWTP Wastewater treatment plant xi Symbols A Surface area of the variable volume tank, soil model block [m 2 ] a Activity of a species or component in the PCM Parameter determining the direct contribution of rainfall falling on ah impermeable surfaces in the catchment area to the flow rate in the sewer ( Rain generator model block) [%] b AOB Decay rate of ammonia oxidising bacteria [1/d] BI Evaluation criteria concerning fate of micropollutants: biotransformation index [%] C Concentration [g/m 3 ] C CJ Concentration of total retransformable parent chemical (micropollutant) [g/m 3 ] CIP gperpeperd Total average daily load of CIP ( Micropollutants model block) [g CIP/(day.1000 PE)] C LI Concentration of parent form of micropollutant [g/m 3 ] CMZ gperpeperd Total average daily load of CMZ ( Micropollutants model block) [g CMZ/(day.1000 PE)] CODpart Particulate Chemical Oxygen Demand [g COD/m 3 ] CODpart gperpeperd Total average daily load of COD particulates per day per PE [g COD/(day.PE)] C SL Concentration of the sorbed form of micropollutant [g/m 3 ] C SL,I Concentration of the sequestered form of micropollutant [g/m 3 ] DCF gperpeperd Total average daily load of DCF ( Micropollutants model block) [g DCF/(day.1000 PE)] DCF S Transition probability matrix of different states of occurrence of DCF ( Micropollutants model block) [ ] DS1 Long term dataset DS2 Short term dataset Eff Overflow of the secondary settler FFfraction Fraction of suspended solids that can settle in the sewer, first flush effect model block [ ] G N2O Quantity of generated nitrous oxide emissions per day [kg N 2 O N/d] G rain_temp Proportional gain to adjust the temperature after a rain event, temperature model block [ ] Enthalpy change of the reaction [kj/mole] Total average daily load of IBU per day per 1000 PE [g IBU gperpeperd IBU/(day.1000PE)] IBU 2OH gperpeperd Total average daily load of IBU 2OH per day per 1000 PE [g IBU 2OH/(day.1000PE)] xii K i Equilibrium constant Time instance in the calculation of the transition probability to k describe the occurrence of the micropollutant with a Markov Chain ( Micropollutants model block) k Bio Biotransformation kinetic parameter [mg/(l.h)] k Bio Biotransformation kinetic parameter [1/h] k Bio,Ax Parameter in ASM X: anoxic biotransformation rate coefficient for SMX, TCY and CIP as C LI [m 3 /(g X SS.day)] k Bio,Ox Parameter in ASM X: aerobic biotransformation rate coefficient for SMX, TCY and CIP as C LI [m 3 /(g X SS.day)] Parameter in ASM X: anoxic biotransformation rate coefficient under k Bio,Ax,Ss growth substrate limiting conditions for DCF and CMZ as C LI [m 3 /(g X SS.day)] Parameter in ASM X: aerobic biotransformation rate coefficient under k Bio,Ox,Ss growth substrate limiting conditions for DCF and CMZ as C LI [m 3 /(g X SS.day)] K D Solid water distribution coefficient [L/g S S ] K D,Ax Parameter in ASM X: anoxic solids liquid sorption coefficient [m 3 /(g X SS )] K down Gain for adjusting the flow rate to downstream aquifers, soil model block [m 2 /d] K D,Ox Parameter in ASM X: aerobic solids liquid sorption coefficient [m 3 /(g X SS )] k Dec Rate constant of retransformation of C CJ to C LI [m 3 /(g X SS.day)] k Dec,Ax Parameter in ASM X: anoxic retransformation rate coefficient for C CJ to C LI [m 3 /(g X SS.day)] k Dec,Ox Parameter in ASM X: aerobic retransformation rate coefficient for C CJ to C LI [m 3 /(g X SS.day)] k Des Parameter in ASM X: Desorption rate coefficient for C SL [1/day] K FA Half saturation coefficient for S FA [g N/m 3 ] K FNA Half saturation coefficient for S FNA [g N/m 3 ] Henry coefficient K i Equilibrium constant of physicochemical model [ ] K I9FA Free ammonia inhibition coefficient for ASMN process no. 9 [g N/m 3 ] Free nitrous acid inhibition coefficient for ASMN process no. 9 [g K I9FNA N/m 3 ] K inf Infiltration gain, soil model block [m 2.5 /d] K L a Volumetric oxygen transfer coefficient [1/d] a Volumetric oxygen transfer coefficient of CO 2 [1/d] xiii K NH2OH,AOB S NH2OH affinity constant for AOB [g N/m 3 ] K NH4,AOB S NH4 affinity constant for AOB [g N/m 3 ] K NH4 Half saturation coefficient for S NH4 [g N/m 3 ] K NO2,AOB S NO2 affinity constant for AOB [g N/m 3 ] K NO,AOB S NO affinity constant for AOB [g N/m 3 ] K NOH Half saturation coefficient for S NOH [g N/m 3 ] K NOH,AOB S NOH affinity constant for AOB [g N/m 3 ] K O2 Parameter in ASM: Half saturation coefficient for S O2 [g/m 3 ] K OA1 Half saturation coefficient for S O2 [g COD/m 3 ] K S Parameter in ASM: Half saturation coefficient for S S [g
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