Hybrid modelling of water resource recovery facilities: status and opportunities

Schneider, Mariane Yvonne; Quaghebeur, Ward; Borzooei, Sina; Froemelt, Andreas; Li, Feiyi; Saagi, Ramesh; Wade, Matthew J.; Zhu, Jun‐Jie; Torfs, Elena

doi:10.2166/wst.2022.115

Cited by 31 publications

(18 citation statements)

References 136 publications

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“…While combining these processes follows a mechanistic approach, the modelling of the disinfection process itself (Chick-Watson) is phenomenological (data-derived). Such hybrid models, building on understanding of mechanisms in combination with data-driven methods, are increasingly used to model water reuse systems, as they combine the advantages of mechanistic (interpretability of results and extrapolation power) with those of data-driven models (e.g., learning unknown relationships) (Schneider et al, 2022).…”

Section: Comparison Of Logistic Regression and A Mechanism-based Mode...mentioning

confidence: 99%

Sensor setpoints that ensure compliance with microbial water quality targets for membrane bioreactor and chlorination treatment in on-site water reuse systems

et al. 2023

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Section: Comparison Of Logistic Regression and A Mechanism-based Mode...mentioning

confidence: 99%

Sensor setpoints that ensure compliance with microbial water quality targets for membrane bioreactor and chlorination treatment in on-site water reuse systems

et al. 2023

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“…35 In addition, despite ASM models being widely accepted, some novel treatment processes, such as anaerobic ammonium oxidation processes 36 and membrane treatment, 37 are still lacking for standard modeling frameworks. 34 Also, digital twins or virtual replicas of water and wastewater treatment infrastructures have been developed. Some examples include simulation platforms such as EPANET for drinking water distribution network, collection systems (info works, SWMM) water-related domain (DHI) and water resources recovery facilities (Biowin, Aquasim, GPS-X, Sumo, Simba, WEST).…”

Section: Mechanistic Wastewater Models: a Piece Of Historymentioning

confidence: 99%

“…Optimization and troubleshooting efforts, and the estimation of resource requirements are other advantages of benchmark calculations using ASM models . Nevertheless, ASM models also have some drawbacks such as model complexity, accuracy, data requirements, high uncertainty due to many simplifications and assumptions, lack of adaptability, insufficient model validation, and computational requirements. − …”

Section: Mechanistic Wastewater Models: a Piece Of Historymentioning

confidence: 99%

“…In addition, despite ASM models being widely accepted, some novel treatment processes, such as anaerobic ammonium oxidation processes and membrane treatment, are still lacking for standard modeling frameworks . Also, digital twins or virtual replicas of water and wastewater treatment infrastructures have been developed.…”

Section: Mechanistic Wastewater Models: a Piece Of Historymentioning

confidence: 99%

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A Review of Computational Modeling in Wastewater Treatment Processes

Duarte,

Martins,

Oliveira

et al. 2023

ACS EST Water

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Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

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“…Wastewater treatment plants (WWTPs) play critical roles in protecting the environment and public health, and digital solutions have demonstrated great promise in modernizing WWTP operation. Using a text mining technique, more than 9,800 studies were found to use or discuss data-driven relevant methods based on a database of 320,000 wastewater-related publications . However, studies found that the digitalization of wastewater sector has been slower than the drinking water industry, primarily because wastewater sensors have lower reliability due to the complexity of wastewater, and the long lag time of biological tests cannot provide real-time monitoring information. , Other factors that lead to the difference are mainly stemmed from different natures of responsibility in treatment and economic incentives.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Optimization for Soft Sensing of Hard-to-Measure Wastewater Key Variables

et al. 2022

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Soft sensors can be an essential part of a digital twin to acquire critical wastewater information for operation optimization. Soft sensor predictions have been successfully applied in nitrogen compounds, but hard-to-measure variables such as biochemical oxygen demand (BOD) and total suspended solids (TSS) have been a major challenge partially due to difficulty in capturing complex nonlinearity and needed information acquisition. This study pinpointed the bottlenecks by developing advanced hyperparameter optimized (HPO) deep learning (DL) models and testing different groups of data. By comparing two DL algorithms [multilayer perceptron and deep belief network (DBN)] with three HPO methods (genetic algorithm, particle swarm optimization (PSO), and grey wolf optimization), we found that DBN-PSO showed performance superior to other hybrid methods for both CBOD5 and TSS predictions based on 11 years of operational data. While the hybrid models exhibit complex topography, better results can be achieved with a slow learning process and a combination of aggressive pre-training and smooth fine-tuning for CBOD5 and TSS, respectively. Additional precipitation data did not provide additional benefits, whereas metal concentration data helped further improve the prediction accuracy (testing error index: 1.9 mg CBOD5/L and 1.5 mg TSS/L), suggesting that more diverse data acquisition is valuable for a better soft-sensor practice.

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Hybrid modelling of water resource recovery facilities: status and opportunities

Cited by 31 publications

References 136 publications

Sensor setpoints that ensure compliance with microbial water quality targets for membrane bioreactor and chlorination treatment in on-site water reuse systems

Sensor setpoints that ensure compliance with microbial water quality targets for membrane bioreactor and chlorination treatment in on-site water reuse systems

A Review of Computational Modeling in Wastewater Treatment Processes

Deep Learning Optimization for Soft Sensing of Hard-to-Measure Wastewater Key Variables

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