Considering The Effect Of Uncertainty And Variability In The Synthetic Generation Of Influent Wastewater Time Series

The availability of influent wastewater time series is crucial for assessing the performance of a wastewater treatment plant (WWTP) under dynamic flow and loading conditions. Given the difficulty of collecting sufficient data, synthetic generation may be the only option. Usually, the main constituents of the influent time series (e.g. flow, COD, TSS, TKN) show periodic, auto-correlation, and cross-correlation structures in time. Therefore researchers have used statistical models (e.g. auto-regressive time series models) for random generation of the influent time series. However, these regular patterns in time could be significantly distorted during rain events (wet weather flow (WWF) conditions) in which the amount and frequency of rainfall affects the flow and other constituents of the influent. To tackle this problem, a hybrid of statistical and conceptual modeling techniques was adopted. The time series of rainfall and influent in DWF conditions (i.e. inputs to the conceptual model) were generated using two types of statistical models (a periodic-multivariate time series model for influent in DWF conditions and a two-state Markov chain-exponential model for rainfall). These two time series serve as inputs to a conceptual model for generation of influent time series during WWF conditions. The effect of total model uncertainty on the generated outputs was also taken into account through a Bayesian calibration and communicated to the user by constructing uncertainty bands with a desired level of confidence. The proposed influent generator is a powerful tool for realistic generation of the influent time series and is well-suited for probabilistic design of WWTPs as it considers both the effect of input variability (i.e. time variation in rainfall and influent composition during DWF) and total model uncertainty in the generation of the influent

Influent generator : towards realistic modelling of wastewater flowrate and water quality using machine-learning methods

Depuis que l'assainissement des eaux usées est reconnu comme un des objectifs de développement durable des Nations Unies, le traitement et la gestion des eaux usées sont devenus plus importants que jamais. La modélisation et la digitalisation des stations de récupération des ressources de l'eau (StaRRE) jouent un rôle important depuis des décennies, cependant, le manque de données disponibles sur les affluents entrave le développement de la modélisation de StaRRE. Cette thèse vis e à faire progresser la modélisation des systèmes d'assainissement en général, et en particulier en ce qui concerne la génération dynamique des affluents. Dans cette étude, différents générateurs d'affluent (GA), qui peuvent fournir un profil d'affluent dynamique, ont été proposés, optimisés et discutés. Les GA développés ne se concentrent pas seulement sur le débit, les solides en suspension et la matière organique, mais également sur les substances nutritives telles que l'azote et le phosphore. En outre, cette étude vise à adapter les GA à différentes applications en fonction des différentes exigences de modélisation. Afin d'évaluer les performances des GA d'un point de vue général, une série de critères d'évaluation de la qualité du modèle est décrite. Premièrement, pour comprendre la dynamique des affluents, une procédure de caractérisation des affluents a été développée et testée pour une étude de cas à l'échelle pilote. Ensuite, pour générer différentes séries temporelles d'affluent, un premier GA a été développé. La méthodologie de modélisation est basée sur l'apprentissage automatique en raison de ses calculs rapides, de sa précision et de sa capacité à traiter les mégadonnées. De plus, diverses versions de ce GA ont été appliquées pour différents cas optimisées en fonction des disponibilités d'études et ont été des données (la fréquence et l'horizon temporel), des objectifs et des exigences de précision. Les résultats démontrent que : i) le modèle GA proposé peut être utilisé pour générer d'affluents dynamiques réalistes pour différents objectifs, et les séries temporelles résultantes incluent à la fois le débit et la concentration de polluants avec une bonne précision et distribution statistique; ii) les GA sont flexibles, ce qui permet de les améliorer selon différents objectifs d'optimisation; iii) les GA ont été développés en considérant l'équilibre entre les efforts de modélisation, la collecte de données requise et les performances du modèle. Basé sur les perspectives de modélisation des StaRRE, l'analyse des procédés et la modélisation prévisionnelle, les modèles de GA dynamiques peuvent fournir aux concepteurs et aux modélisateurs un profil d'affluent complet et réaliste, ce qui permet de surmonter les obstacles liés au manque de données d'affluent. Par conséquent, cette étude a démontré l'utilité des GA et a fait avancer la modélisation des StaRRE en focalisant sur l'application de méthodologies d'exploration de données et d'apprentissage automatique. Les GA peuvent donc être utilisés comme outil puissant pour la modélisation des StaRRE, avec des applications pour l'amélioration de la configuration de traitement, la conception de procédés, ainsi que la gestion et la prise de décision stratégique. Les GA peuvent ainsi contribuer au développement de jumeaux numériques pour les StaRRE, soit des système intelligent et automatisé de décision et de contrôle.Since wastewater sanitation is acknowledged as one of the sustainable development goals of the United Nations, wastewater treatment and management have been more important then ever. Water Resource Recovery Facility (WRRF) modelling and digitalization have been playing an important role since decades, however, the lack of available influent data still hampers WRRF model development. This dissertation aims at advancing the field of wastewater systems modelling in general, and in particular with respect to the dynamic influent generation. In this study, different WRRF influent generators (IG), that can provide a dynamic influent flow and pollutant concentration profile, have been proposed, optimized and discussed. The developed IGs are not only focusing on flowrate, suspended solids, and organic matter, but also on nutrients such as nitrogen and phosphorus. The study further aimed at adapting the IGs to different case studies, so that future users feel comfortable to apply different IG versions according to different modelling requirements. In order to evaluate the IG performance from a general perspective, a series of criteria for evaluating the model quality were evaluated. Firstly, to understand the influent dynamics, a procedure of influent characterization has been developed and experimented at pilot scale. Then, to generate different realizations of the influent time series, the first IG was developed and a data-driven modelling approach chosen, because of its fast calculations, its precision and its capacity of handling big data. Furthermore, different realizations of IGs were applied to different case studies and were optimized for different data availabilities (frequency and time horizon), objectives, and modelling precision requirements. The overall results indicate that: i) the proposed IG model can be used to generate realistic dynamic influent time series for different case studies, including both flowrate and pollutant concentrations with good precision and statistical distribution; ii) the proposed IG is flexible and can be improved for different optimization objectives; iii) the IG model has been developed by considering the balance between modelling efforts, data collection requirements and model performance. Based on future perspectives of WRRF process modelling, process analysis, and forecasting, the dynamic IG model can provide designers and modellers with a complete and realistic influent profile and this overcomes the often-occurring barrier of shortage of influent data for modelling. Therefore, this study demonstrated the IGs' usefulness for advanced WRRF modelling focusing on the application of data mining and machine learning methodologies. It is expected to be widely used as a powerful tool for WRRF modelling, improving treatment configurations and process designs, management and strategic decision-making, such as when transforming a conventional WRRF to a digital twin that can be used as an intelligent and automated system

Generation of (synthetic) influent data for performing wastewater treatment modelling studies

The success of many modelling studies strongly depends on the availability of sufficiently long influent time series - the main disturbance of a typical wastewater treatment plant (WWTP) - representing the inherent natural variability at the plant inlet as accurately as possible. This is an important point since most modelling projects suffer from a lack of realistic data representing the influent wastewater dynamics. The objective of this paper is to show the advantages of creating synthetic data when performing modelling studies for WWTPs. This study reviews the different principles that influent generators can be based on, in order to create realistic influent time series. In addition, the paper summarizes the variables that those models can describe: influent flow rate, temperature and traditional/emerging pollution compounds, weather conditions (dry/wet) as well as their temporal resolution (from minutes to years). The importance of calibration/validation is addressed and the authors critically analyse the pros and cons of manual versus automatic and frequentistic vs Bayesian methods. The presentation will focus on potential engineering applications of influent generators, illustrating the different model concepts with case studies. The authors have significant experience using these types of tools and have worked on interesting case studies that they will share with the audience. Discussion with experts at the WWTmod seminar shall facilitate identifying critical knowledge gaps in current WWTP influent disturbance models. Finally, the outcome of these discussions will be used to define specific tasks that should be tackled in the near future to achieve more general acceptance and use of WWTP influent generators

Probabilistic design and upgrade of wastewater treatment plants in the EU Water Framework directive context

The EU Water Framework Directive requires compliance with effluent and receiving water quality standards. This increased complexity implies that the evaluation of the impact of measures should be evaluated with adequate tools, both from the methodological point of view – by applying systems analysis investigations and modelling uncertainty assessment tools – and by making the developed methodology applicable in practice. Urban wastewater systems (UWWSs) are crucial components of river basins, since they usually contribute significantly to the pollution loads. They also have more flexibility in operation and management than other subsystems as agriculture. One part of this dissertation tries to answer the question “where” to improve the UWWS in a basin by means of systems analysis. A case study is tackled with the help of substance flow analysis (SFA) and of performance indicators. SFA allowed to identify the pressures on the receiving water. The indicators highlighted the critical structures in the basin. The spatial scale of the study was found to be of paramount importance. The other part of this dissertation deals with the question “how” to improve the UWWS, by proposing a systematic methodology to design correction measures, illustrated by the example of WWTP design and upgrade. The first step is the generation of influent time series to be fed to the WWTP models by means of a new phenomenological model of the draining catchment and sewer system. Ten different treatment process configurations were selected for the comparison. Further, eleven upgrade options were selected for evaluation, partly requiring real-time control (RTC) and partly the construction of additional treatment volume. For the immission-based evaluation, the integration of the WWTP model with a river model was made by means of the continuity-based interfacing method (CBIM). The propagation of the uncertainty on model parameters was performed with Monte Carlo simulations. Given the assumed boundary conditions, alternating systems show the best treatment cost-efficiency. RTC upgrades showed good potential for low-cost compliance, but with higher risk of limits exceedance. The immission-based evaluation revealed that considering the system from a holistic point of view can lead to substantial savings

ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

Integrated Active Control Strategies and Licensing Approaches for Urban Wastewater Systems

The wastewater sector in the UK and other EU member states are facing stringent regulatory standards. The environmental water quality standards such as the EU-WFD, on the one hand, require a higher level of wastewater treatment which can result in increased GHG emissions and operational cost through higher energy use, chemical consumption, and capital investment. On the other hand, the Carbon Reduction Commitment Energy Efficiency scheme requires the water industries to reduce their GHG emission significantly. The research assesses the advantage of integrated active control of existing WWTPs, their optimisation and dynamic licensing approach to tackle this challenge while maintaining the quality of the receiving river. The dynamic licensing approach focuses on the design of control strategies based on the receiving river’s assimilative capacity. A simulation approach is used to test control strategies and their optimisation, interventions, and dynamic licensing approaches. The study developed an integrated UWWS model that fully integrate WWTP, sewer network, and receiving river, which enables the assessment of the advantage of integrated control strategies and dynamic licensing approach. The hybrid modelling approach uses mechanistic, conceptual and data-driven models in order to reduce computational cost while maintaining the model accuracy. Initially, the WWTP model was set up using average values of model parameters from the literature. However, this did not give a model with good accuracy. Hence, through, a careful design and identification of key parameters, a data campaign was designed to characterise influent wastewater, flow pattern, and biological processes of a real-world case study. The model accuracy was further improved using auto-calibration processes using a sensitivity analysis, identifying influential parameters to which the final effluent and oxidation ditch quality indicators are sensitive to. The sensitivity and auto-calibration were done using statistical measures that compare simulated and measured data points. Nash-Sutcliff coefficient (NSE) and root-mean-square-error (RMSE) measures show consistency in the sensitivity analysis, but correlation coefficient R2 showed a slight difference as it focusses on pattern similarity than values closeness. The combined use of NSE and RMSE gave the best result in model accuracy using fewer generation in the multi-objective optimisation using NSGA-II. Further local sensitivity analysis is used to identify the effect of varying control handles on GHG emissions (as equivalent CO2 emission), operational cost and effluent quality. The GHG emissions both from direct and indirect sources are considered in this study. The indirect GHG emissions consider the major GHG emissions (CO2, N2O, and CH4) associated with the use of electricity, sludge transport, and offsite degradation of sludge and final effluent. Similarly, the direct GHG emissions consider the emission of these major gases from different biological processes within the WWTP such as substrate utilisation, denitrification and biomass decay. This knowledge helps in the development of control strategies by indicating influential control handles and aids the selection of control strategies for optimisation purposes. It is found that multi-objective optimisation can reduce GHG emissions, operational cost while operating under the effluent quality standards. Multi-objective optimisation of control loops coupled with integrated active control of oxygen using final effluent ammonia concentration showed the highest reduction in GHG emissions and reduction in operational cost without violating the current effluent quality standard. Through dynamic licensing approach, the oxygen level in the oxidation ditch is controlled based on the assimilative capacity of the receiving river, which reduces the operational cost and effluent quality index without increased GHG emissions. However, to benefit from the dynamic licensing approach, a trade-off needs to be considered further between final effluent NO3 concentration and reduction in oxygen level in the oxidation ditch to reduce biomass decay which is responsible for higher GHG emission in this scenario