A Recurrent Neural Network for Wastewater Treatment Plant Effuents' Prediction

[Abstract] Wastewater Treatment Plants (WWTP) are industries devoted to process water coming from cities' sewer systems and to reduce their contamination. High-pollutant products are generated in the pollutant reduction processes. For this reason, certain limits are established and violations of them are translated into high economic punishments and environmental problems. In this paper data driven methods are performed to monitor the WWTP behaviour. The aim is to predict its effluent concentrations in order to reduce possible violations and their derived costs. To do so, an alarm generation system based on the application of Artificial Neural Networks (ANNs) is proposed. The proposed system shows a good prediction accuracy (errors around 5%) and a reduced miss-detection probability (30%).[Resumen] Las Plantas de tratamiento de aguas residuales (PTAR) son industrias dedicadas a procesar el agua que proviene de los sistemas de alcantarillado de las ciudades y reducir su contaminación. Los productos de alta contaminación se generan en los procesos de reducción de contaminantes. Por esta razón, se establecen ciertos límites y sus violaciones se traducen en castigos económicos elevados y problemas ambientales. En este documento, se realizan métodos controlados por datos para monitorizar el comportamiento de la EDAR. El objetivo es predecir sus concentraciones de efluentes para reducir las posibles violaciones y sus costos derivados. Para ello, se propone un sistema de generación de alarmas basado en la aplicación de Redes Neuronales Artificiales (ANN). El sistema propuesto muestra una buena precisión de predicción (errores en torno al 5%) y una probabilidad de detección errónea reducida (30%).Ministerio de Economía y Empresa; DPI2016-77271-

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

Design of feedback control strategies in a plant-wide wastewater treatment plant for simultaneous evaluation of economics, energy usage, and removal of nutrients

Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass trans-fer coefficient (KLa). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the KLa of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the last aerobic reactor as a feedback signal to alter the DO set-points. PI-fuzzy showed improved effluent quality by 21.1%, total phosphorus removal rate by 33.3% with an increase of operational cost, and a slight increase in the production rates of greenhouse gases. In all the control design frameworks, a trade-off is observed between operational cost and effluent quality

Modelling and techno-economic assessment of (bio)electrochemical nitrogen removal and recovery from reject water at full WWTP scale

At conventional wastewater treatment plants (WWTPs), reject waters originating from the dewatering of anaerobically digested sludge contain the highest nitrogen concentrations within the plant and thereby have potential for realising nitrogen recovery in a reusable form. At the same time, nitrogen removal from reject waters has potential to reduce the energetic and chemical demands of the WWTP due to a reduced nutrient load to the activated sludge process. In recent years, (bio)electrochemical methods have been extensively studied for nitrogen recovery from reject waters in laboratory-scale but not yet implemented in real WWTP environments, particularly due to concerns about the need for large capital investments. This study assessed the techno-economic feasibility of retrofitting a (bio)electrochemical nitrogen removal and recovery (NRR) unit into the reject water circulation line of a full-scale WWTP through modelling. Data from laboratory-scale (bio)electroconcentration ((B)EC) experiments was used to construct a simple, semi-empirical model block integrated into the Benchmark Simulation Model No. 2 (BSM2) simulating a generalised WWTP. The effects of nitrogen removal from the reject water on both the effluent quality and operational costs of the WWTP were assessed and compared to the BSM2 performance without an NRR unit. In all studied scenarios, the effluent quality index was improved by 4–11%, while both the aeration (7–19% decrease) and carbon (24–71%) requirements were reduced. The additional energy consumed by the NRR unit increased the total operational cost index by >18%, but the revenue assumed for the generated nutrient product (20 EUR kgN−1) was enough to make the BEC-NRR scenarios at realistically low current densities (1 and 5 A m−2) economically attractive compared to the control. A sensitivity analysis revealed that electricity price and nutrient product value had the most notable effects on the feasibility of the NRR unit. The results suggest a key factor in making (bio)electrochemical NRR economically viable is to reduce its electricity consumption further, while the anticipated increases in nitrogen fertiliser prices can help accelerate the adoption of these methods in larger scale.publishedVersionPeer reviewe

LSTM-Based Wastewater Treatment Plants Operation Strategies for Effluent Quality Improvement

Wastewater Treatment Plants (WWTPs) are facilities devoted to managing and reducing the pollutant concentrations present in the urban residual waters. Some of them consist in nitrogen and phosphorus derived products which are harmful for the environment. Consequently, certain constraints are applied to pollutant concentrations in order to make sure that treated waters comply with the established regulations. In that sense, efforts have been applied to the development of control strategies that help in the pollutant reduction tasks. Furthermore, the appearance of Artificial Neural Networks (ANNs) has encouraged the adoption of predictive control strategies. In such a fashion, this work is mainly focused on the adoption and development of them to actuate over the pollutant concentrations only when predictions of effluents determine that violations will be produced. In that manner, the overall WWTP's operational costs can be reduced. Predictions are generated by means of an ANN-based Soft-Sensor which adopts Long-Short Term Memory cells to predict effluent pollutant levels. These are the ammonium (S-{NH,e}) and the total nitrogen (S-{Ntot,e}) which are predicted considering influent parameters such as the ammonium concentration at the entrance of the WWTP reactor tanks (S-{NH,po}), the reactors' input flow rate (Q-{po}), the WWTP recirculation rate (Q-{a}) and the environmental temperature (T-{as}). Moreover, this work presents a new multi-objective control scenario which consists in a unique control structure performing the reduction of S-{NH,e} and S-{Ntot,e} concentrations simultaneously. Performance of this new control approach is contrasted with other strategies to determine the improvement provided by the ANN-based Soft-Sensor as well as by the fact of being controlling two pollutants at the same time. Results show that some brief and small violations are still produced. Nevertheless, an improvement in the WWTPs performance w.r.t.The most common control strategies around 96.58% and 98.31% is achieved for S-{NH,e} and S-{Ntot,e}, respectively

Greenhouse gas emissions from and storm impacts on wastewater treatment plants : process modelling and control

Cette thèse étudie l'interaction entre les stations d’épuration (STEP) et le changement climatique: soit en premier lieu la production ainsi que les émissions de gaz à effet de serre (GES), en particulier le protoxyde d’azote (N2O), généré à la STEP et en second lieu l’effet des pluies plus intenses dues aux changements climatiques sur la STEP. Des campagnes de mesure sur le terrain et la modélisation à échelle réelle ont été utilisées conjointement dans cette recherche. Une campagne de mesure d'une durée d’un mois a été réalisée dans une STEP traitant les eaux usées de 750,000 équivalents habitants, soit la STEP d’Eindhoven aux Pays-Bas. Des capteurs en ligne ont été installés dans la zone d'aération du bioréacteur. Une usine virtuelle de grande échelle, soit la STEP décrit par le Benchmark Simulation Model No.2 (BSM2), ainsi qu’une usine réelle de grande échelle, soit la STEP d’Eindhoven aux Pays-Bas, étaient incluses dans cette étude. Dans les deux cas, les modèles ont été modifiés afin de prendre en compte les GES, en particulier la production de N2O. Deux modèles de boues activées (ASM) ont été développés, soit l’ASMG1 et l’ASMG2d. En plus de la conversion de N2O par les bactéries hétérotrophes, les deux modèles sont en mesure de simuler la production de N2O par la dénitrification catalysée par les bactéries oxydant l'ammoniac (AOB). Les modèles décrivent aussi l'effet de l’oxygène dissous (OD) sur la cinétique de production de N2O par les AOB grâce à une modification de la cinétique d’Haldane. Les résultats montrent que les AOB produisent beaucoup de N2O tandis que les hétérotrophes en consomment considérablement. Les émissions de N2O augmentent lorsque les concentrations de NH4+ sont élevées et que les concentrations d’OD sont modérées (jusqu’à 2.5 mg O2/l dans cette étude). Ces conditions peuvent avoir été créées par le contrôle en cascade de NH4+-OD qui vise à réduire la consommation d'énergie en diminuant les concentrations d'OD lorsque la concentration de NH4+ est suffisamment faible. En outre, ce contrôleur en cascade est une stratégie de rétroaction à gain faible. C'est-à-dire, un retard significatif se produit entre la détection d'une augmentation de NH4+ et l'accroissement de l'aération. Toutes ces propriétés produisent des conditions favorables à la production de N2O par les bactéries AOB. Différents scénarios alternatifs ainsi que des stratégies de contrôle ont été comparés selon la qualité de l'effluent, le coût d’opération et les émissions de GES. Dans le cadre de BSM2, un bon équilibre entre la qualité de l'effluent, le coût d’opération et les émissions de GES a été obtenu avec à la mise en œuvre d'un contrôleur rétroactif pur de l’OD sur la première zone d'aération et d’un contrôleur en cascade de NH4+-DO sur les deux zones d'aération suivantes et en utilisant soit une stratégie d'alimentation étagée ou le contrôle du recyclage des boues afin de gérer les pics de débits. Mots-clés: Traitement des eaux usées par boues activées, contrôle de procédé, campagne de mesures en terrain, modélisation mathématique à échelle grandeur réelle, gaz à effet de serre, protoxyde d’azote, temps de pluie.This PhD thesis studied the interaction between wastewater treatment plants (WWTPs) and climate change, i.e. the production and emission of greenhouse gases (GHGs), especially nitrous oxide (N2O), from WWTPs and the effect of the climate change induced more intense rain events on WWTPs. Both field measurements and full-scale modelling were pursued in this research. A one-month measurement campaign was performed by installing on-line sensors at the aeration zone of the bioreactor of a 750,000 person equivalents WWTP, i.e. the Eindhoven WWTP in the Netherlands. The models of a full-scale virtual plant, i.e. the Benchmark Simulation Model No.2 (BSM2), and a full-scale real plant, i.e. the Eindhoven WWTP in the Netherlands, were extended with respect to GHG emissions, especially the pathways involving N2O. Two types of extended Activated Sludge Models (ASM) were developed, i.e. ASMG1 for COD/N removal and ASMG2d for COD/N/P removal. Besides heterotrophic N2O production, both proposed models include N2O production by nitrite denitrification by ammonia-oxidizing bacteria (AOB) and describe the DO effect on AOB N2O production by a modified Haldane kinetics term. Results showed that AOB are the major producer of N2O while the heterotrophs consume N2O considerably. The high N2O emissions occurred under high NH4+ and intermediate DO concentrations (up to 2.5 mg O2/l in this work). Such conditions can be created by NH4+-DO cascade control which aims at reducing energy consumption by lowering the DO concentrations when the NH4+ concentration is sufficiently low. Moreover, this cascade controller is a low-gain feedback control strategy, i.e. a significant delay will occur between the detection of a NH4+ increase and the increase in aeration. All these properties lead to conditions favourable to N2O production by AOB. Different alternative scenarios and control strategies were compared in terms of effluent quality, operational cost and GHG emissions. In the framework of BSM2, a good balance among effluent quality, operational cost and GHG emissions was realized by implementing a pure DO feedback controller in the first aeration zone and a NH4+-DO cascade controller in the following two aeration zones and using either step feed or sludge recycling control to deal with hydraulic shocks. Keywords: Activated sludge, wastewater treatment, process control, field measurements, full-scale mathematical modelling, greenhouse gases, nitrous oxide, wet weather conditions