Modeling spatiotemporal domestic wastewater variability:Implications for measuring treatment efficiency

Continuously measuring the efficiency of wastewater treatment plants is crucial to progress in sanitation man- agement. Regulations for decentralized wastewater treatment plants (WWTP) can include rudimentary specifi- cations for sporadic sampling, unencouraging continuous monitoring, and missing crucial domestic wastewater (DW) variability, especially in low- and middle-income countries. However, few studies have focused on modeling and understanding spatiotemporal DW variability. We developed and calibrated an agent-based model (ABM) to understand spatial and temporal DW variability, its role in estimated WWTP efficiency, and provide recommendations to improve sampling regulations. We simulated DW variability at various spatial and temporal resolutions in Santa Ana Atzcapotzaltongo, Mexico, focusing on chemical oxygen demand (COD) and total suspended solids (TSS). The model results show that DW variability increases at higher spatiotemporal resolu- tions. Without a proper understanding of DW variability, treatment efficiency can be overestimated or under- estimated by as much as 25% from sporadic sampling. Sensor measurements at 6-min intervals over 3 hours are recommended to overcome uncertainty resulting from temporal variability during heavy drinking water demand in the morning. Reporting of sewage catchment areas, population sizes, and sampling times and intervals is recommended to compare WWTP efficiencies to overcome uncertainty resulting from spatiotemporal variability. The proposed model is a useful tool for understanding DW variability. It can be used to estimate the impact of spatiotemporal variability when measuring WWTP efficiencies, support improvements to sampling regulations for decentralized sanitation, and alternatively for designing and operating WWTPs

An assessment of urban stormwater runoff influence on river water quality. A case study: Padua watewways

Lo scopo della tesi è la creazione di un modello matematico per valutare l'impatto che il runoff della città di Padova ha sulla qualità dell'acqua della propria rete fluvial

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

Development of a risk based model for use in water quality monitoring

Modelling has recently emerged as an effective and efficient tool in the area of water quality monitoring with new models taking in vast quantities of data and facilitating the development of more targeted water monitoring programs. With the Water Framework Directive demanding that monitoring requirements for a list of priority substances be met, achieving ‘good’ status in all water bodies by 2015, there is a strong need for improved monitoring programmes. In order to improve future monitoring programmes by making the process more ‘targeted’ a simple risk-based model for the occurrence of priority substances in wastewater treatment plant effluent was devised. This model was developed through the collection of an extensive list of documents relating to priority substances emission factors. These included wastewater treatment licence applications, trade effluent licences, traffic data, rainfall data and census data. It was found that by relating data from each of these sources to historic occurrence data it was possible to conceptualise and develop to a model of risk of occurrence of priority substances. Validation of this model was carried out using data from a 24 month sampling plan at 9 sites in two counties in Ireland. This work has allowed for the compilation of a large dataset of emission factor and priority substance occurrence in Ireland where none previously existed. For the first time a risk-based model has been developed for Irish wastewater treatment plant effluents. Together the model and dataset can be used by policy makers and inform the development of future priority substance monitoring programmes