A review of computational modeling in wastewater treatment processes
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/89451 |
Resumo: | 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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A review of computational modeling in wastewater treatment processesAlgorithmsChemical structureEnvironmental modelingQuality managementWater treatmentÁgua potável e saneamentoWastewater 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.This work was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the PAMWater Project (DSAIPA/Al/0099/2019), the AIM4- Water Project (2022.06822.PTDC), and the strategic funding of UIDB/04469/2020 and UIDB/00319/2020 units. The work of P.O. was supported by the doctoral Grant PRT/BD/154311/2022 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from European Union, under MIT Portugal Program.info:eu-repo/semantics/publishedVersionAmerican Chemical SocietyUniversidade do MinhoDuarte, Maria SaloméMartins, GilbertoOliveira, PedroFernandes, BrunoFerreira, Eugénio C.Alves, M. M.Lopes, FredericoPereira, M. A.Novais, Paulo2024-032024-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/89451engDuarte, M. S., Martins, G., Oliveira, P., Fernandes, B., Ferreira, E. C., Alves, M. M., … Novais, P. (2023, August 24). A Review of Computational Modeling in Wastewater Treatment Processes. ACS ES&T Water. American Chemical Society (ACS). http://doi.org/10.1021/acsestwater.3c001172690-063710.1021/acsestwater.3c00117https://pubs.acs.org/journal/aewcaainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-16T01:20:10Zoai:repositorium.sdum.uminho.pt:1822/89451Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:01:02.873450Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A review of computational modeling in wastewater treatment processes |
title |
A review of computational modeling in wastewater treatment processes |
spellingShingle |
A review of computational modeling in wastewater treatment processes Duarte, Maria Salomé Algorithms Chemical structure Environmental modeling Quality management Water treatment Água potável e saneamento |
title_short |
A review of computational modeling in wastewater treatment processes |
title_full |
A review of computational modeling in wastewater treatment processes |
title_fullStr |
A review of computational modeling in wastewater treatment processes |
title_full_unstemmed |
A review of computational modeling in wastewater treatment processes |
title_sort |
A review of computational modeling in wastewater treatment processes |
author |
Duarte, Maria Salomé |
author_facet |
Duarte, Maria Salomé Martins, Gilberto Oliveira, Pedro Fernandes, Bruno Ferreira, Eugénio C. Alves, M. M. Lopes, Frederico Pereira, M. A. Novais, Paulo |
author_role |
author |
author2 |
Martins, Gilberto Oliveira, Pedro Fernandes, Bruno Ferreira, Eugénio C. Alves, M. M. Lopes, Frederico Pereira, M. A. Novais, Paulo |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Duarte, Maria Salomé Martins, Gilberto Oliveira, Pedro Fernandes, Bruno Ferreira, Eugénio C. Alves, M. M. Lopes, Frederico Pereira, M. A. Novais, Paulo |
dc.subject.por.fl_str_mv |
Algorithms Chemical structure Environmental modeling Quality management Water treatment Água potável e saneamento |
topic |
Algorithms Chemical structure Environmental modeling Quality management Water treatment Água potável e saneamento |
description |
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. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-03 2024-03-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/89451 |
url |
https://hdl.handle.net/1822/89451 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Duarte, M. S., Martins, G., Oliveira, P., Fernandes, B., Ferreira, E. C., Alves, M. M., … Novais, P. (2023, August 24). A Review of Computational Modeling in Wastewater Treatment Processes. ACS ES&T Water. American Chemical Society (ACS). http://doi.org/10.1021/acsestwater.3c00117 2690-0637 10.1021/acsestwater.3c00117 https://pubs.acs.org/journal/aewcaa |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
American Chemical Society |
publisher.none.fl_str_mv |
American Chemical Society |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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