Modelling and model-based optimization of N-removal WRRFs : reactive settling, conventional & short-cut N-removal processes

La détérioration des ressources en eau et la grande quantité d'eau polluée générée dans les sociétés industrialisées donnent une importance fondamentale aux procédés de traitement des eaux usées pour préserver les ressources, conformément à l'objectif 6 des 17 objectifs de développement durable des Nations Unies. Le rejet de nutriments tels que l'ammoniac par les eaux usées est un problème important, l'élimination de l'azote (N) est donc l'un des processus critiques de toute station de récupération des ressources en eau (StaRRE). L'objectif de ce projet de recherche doctoral est d'améliorer la compréhension des mécanismes d'élimination de l'azote dans le traitement biologique des eaux usées grâce à la modélisation, et d'optimiser les StaRRE existantes pour réduire la consommation d'énergie et de ressources. Dans ce cadre, 3 études différentes ont été réalisées. Tout d'abord, un modèle de décanteur réactif unidimensionnel a été développé. Celui-ci prédit le comportement de décantation de boues à des concentrations élevées de boues ainsi que les conversions biocinétiques dans le processus de décantation secondaire (DS). Il a été constaté qu'une description précise des réactions biocinétiques dans la DS impose des défis de calibration élevés pour le modèle de décantation, car ce dernier doit capturer les profils de concentration complets de la biomasse active dans la couverture de boues. Le modèle calibré a pu prédire avec précision les profils de concentration des effluents et du lit de boues dans la DS. Le modèle développé peut être utilisé pour le contrôle et la simulation des StaRRE afin d'obtenir de meilleures prédictions des concentrations d'effluents et des boues de retour, et aussi de calculer correctement le bilan massique d'azote d'une StaRRE. Deuxièmement, un modèle à l'échelle de l'usine a été mis en place pour un système de pré-dénitrification conventionnel pour la StaRRE pilEAUte à l'échelle pilote. Une méthodologie de calibration du modèle par étapes a été adoptée en fusionnant les principaux protocoles de calibration de modèle, tout en mettant l'accent sur le modèle biocinétique. Le modèle de la StaRRE pilEAUte, y compris le décanteur réactif développé, a été calibré et validé pour simuler les variables de modèle sélectionnées, puis utilisé pour une analyse de scénarios plus approfondie de l'optimisation de la consommation d'énergie et des ressources. Les résultats de l'analyse des scénarios ont montré le potentiel d'optimisation du système conventionnel d'élimination d'azote grâce à la réduction de l'aération et du retour interne des nitrates. Ils ont également démontré que la dénitrification dans le décanteur secondaire peut avoir une contribution significative à la capacité globale d'élimination d'azote d'une StaRRE lorsque la liqueur mixte peut traverser le lit de boues. Troisièmement, l'étude visait à évaluer l'applicabilité des stratégies de commande continu et intermittent du rapport de l'ammoniac par rapport aux NOX-N (commande AvN) sur la StaRRE pilEAUte. Les stratégies de commande de l'aération par AvN sont appliquées en amont d'un réacteur de désammonification, qui est un processus d'élimination efficace d'azote avec un besoin de ressources réduit (en termes d'aération et carbone) par rapport aux systèmes conventionnels. Les deux stratégies de commande testées pourraient être réalisées grâce à une commande automatique. Cependant, le maintien du rapport AvN dans l'effluent à la valeur souhaitée (1) dépend fortement des conditions opérationnelles telles que les variations de l'affluent, le temps de rétention des boues et la fiabilité des capteurs. Même si la recherche est guidée par les études de StaRRE à l'échelle pilote, les méthodologies développées pour démontrer et modéliser les processus et les conditions opérationnelles économes en énergie et en ressources sont applicables et transférables à d'autres études de cas à plein échelle.Deterioration of water resources and the large amount of polluted water generated in industrialized societies gives fundamental importance to waste water treatment processes to preserve resources in accordance with goal 6 of the 17 sustainable development goals of the United Nations. Discharge of nutrients such as ammonia with waste water is a significant issue, thus nitrogen (N) removal is one of the critical processes of any water resource recovery facilities (WRRF). The objective of this PhD research project was to improve the understanding of N-removal mechanisms in biological treatment of wastewater through modelling and to optimize existing WRRFs to reduce energy and resource consumption. Within this context, 3 different studies were carried out. First, a one dimensional reactive settler model was developed that predicts the settling behaviour at high sludge concentrations together with biokinetic conversions in the secondary settling process. It was found that an accurate description of biokinetic reactions in the SST puts high calibration requirements on the settling model as it must properly capture the full concentration profiles of active biomass in the sludge blanket. The calibrated model was able to accurately predict the effluent and sludge blanket concentration profiles in the SST. The developed model can be used for control and simulation of WRRFs for better predictions of SST effluent and underflow concentrations and also properly calculate the nitrogen mass balance of a WRRF. Second, a plant-wide model was set up for a conventional pre-denitrification system for the pilot-scale pilEAUte WRRF. A step-wise model calibration methodology was adopted by merging main existing model calibration protocols while placing emphasis on the biokinetic model. The pilEAUte model, including the developed reactive settler, was calibrated and validated to simulate the selected model variables and used for further scenario analysis for energy and resource optimization. The scenario analysis results showed the optimization potential of conventional N removal systems through application of reduced aeration and internal nitrate recycling. It also demonstrated that denitrification in the secondary settler can contribute significantly to the overall N removal capacity of the WRRF when mixed liquor can pass through the sludge blanket. Third, it was aimed to evaluate the applicability of continuous and intermittent Ammonia vs NOₓ-N (AvN) control strategies on the pilEAUte WRRF. The AvN aeration control strategies are applied prior to a deammonification stage which is a short-cut N removal process with reduced resource (aeration and carbon) requirements in comparison to conventional systems. Both strategies could be achieved through automatic control. However, keeping the AvN ratio in the effluent on the desired value highly depends on operational conditions such asinfluent variations, sludge retention time and the sensor's measurement reliability

Mainstream short-cut N removal modelling: current status and perspectives

This work gives an overview of the state-of-the-art in modelling of short-cut processes for nitrogen removal in mainstream wastewater treatment and presents future perspectives for directing research efforts in line with the needs of practice. The modelling status for deammonification (i.e., anammox-based) and nitrite-shunt processes is presented with its challenges and limitations. The importance of mathematical models for considering N2O emissions in the design and operation of short-cut nitrogen removal processes is considered as well. Modelling goals and potential benefits are presented and the needs for new and more advanced approaches are identified. Overall, this contribution presents how existing and future mathematical models can accelerate successful full-scale mainstream short-cut nitrogen removal applications

An improved 1D reactive Burger-Diehl settler model for secondary settling tank denitrification

An improved 1D reactive settler model is pursued in order to increase the understanding of reactive settling processes and obtain a better prediction of the nitrogen mass balance in wastewater treatment systems. The developed model is based on the 1D Burger-Diehl settler model with compression function and the Activated Sludge Model No. 1 biological reactions. Specific attention was paid in the model development phase to optimal selection of settling velocity functions and integration of the correct clarifier geometry. A unique measurement campaign was carried out with different operational scenarios to quantify the denitrification in a secondary settling tank. A detailed step-wise calibration effort demonstrated that by choosing an appropriate settling velocity function (power-law structure) and considering the true clarifier geometry allows to accurately capture the biomass concentration profile, total sludge mass, sludge blanket height, and the reaction rates. The resulting model is able to accurately describe total suspended solids (TSS) and nitrate concentration profiles throughout a settling tank under different operational conditions. As such the model can be applied in further scenario analysis and system optimization. Practitioner Points A unique measurement campaign was carried out to obtain detailed data for a reactive settler model development.A 1-D reactive settler model is developed based on the Burger-Diehl framework including ASM1 biokinetics and the clarifier geometry.An extensive calibration and model selection effort was performed. The model accurately predicts measured concentration profiles in the settling tank.The developed model can be integrated in a plant-wide model to properly calculate the nitrogen mass balance of a WRRF

Digital solutions for continued operation of WRRFs during pandemics and other interruptions

This paper includes survey results from 17 full-scale water resource recovery facilities (WRRFs) to explore their technical, operational, maintenance, and management-related challenges during COVID-19. Based on the survey results, limited monitoring and maintenance of instrumentation and sensors are among the critical factors during the pandemic which resulted in poor data quality in several WRRFs. Due to lockdown of cities and countries, most of the facilities observed interruptions of chemical supply frequency which impacted the treatment process involving chemical additions. Some plants observed influent flow reduction and illicit discharges from industrial wastewater which eventually affected the biological treatment processes. Delays in equipment maintenance also increased the operational and maintenance cost. Most of the plants reported that new set of personnel management rules during pandemic created difficulties in scheduling operator's shifts which directly hampered the plant operations. All the plant operators mentioned that automation, instrumentation, and sensor applications could help plant operations more efficiently while working remotely during pandemic. To handle emergency circumstances including pandemic, this paper also highlights resources and critical factors for emergency responses, preparedness, resiliency, and mitigation that can be adopted by WRRFs