Hybrid modelling of water resource recovery facilities: status and opportunities.


Journal

Water science and technology : a journal of the International Association on Water Pollution Research
ISSN: 0273-1223
Titre abrégé: Water Sci Technol
Pays: England
ID NLM: 9879497

Informations de publication

Date de publication:
May 2022
Historique:
entrez: 16 5 2022
pubmed: 17 5 2022
medline: 19 5 2022
Statut: ppublish

Résumé

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

Identifiants

pubmed: 35576250
pmc: wst_2022_115
doi: 10.2166/wst.2022.115
doi:

Substances chimiques

Waste Water 0
Water 059QF0KO0R

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2503-2524

Auteurs

Mariane Yvonne Schneider (MY)

Next Generation Artificial Intelligence Research Center & School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan E-mail: myschneider@isi.imi.i.u-tokyo.ac.jp.

Ward Quaghebeur (W)

Centre for Advanced Process Technology for Urban Resource recovery (CAPTURE), Frieda Saeysstraat 1, Gent 9000, Belgium; BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent 9000, Belgium; KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent 9000, Belgium.

Sina Borzooei (S)

Centre for Advanced Process Technology for Urban Resource recovery (CAPTURE), Frieda Saeysstraat 1, Gent 9000, Belgium; BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent 9000, Belgium.

Andreas Froemelt (A)

Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf 8600, Switzerland.

Feiyi Li (F)

modelEAU, CentrEau, Département de génie civil et de génie des eaux, Pavillon Adrien-Pouliot, Université Laval, Quebec City, Canada.

Ramesh Saagi (R)

Division of Industrial Electrical Engineering and Automation (IEA), Department of Biomedical Engineering, Lund University, P.O. Box 118, Lund SE-22100, Sweden.

Matthew J Wade (MJ)

School of Engineering, Newcastle University, Newcastle-upon-Tyne NE1 7RU, UK.

Jun-Jie Zhu (JJ)

Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ 08544, USA.

Elena Torfs (E)

Centre for Advanced Process Technology for Urban Resource recovery (CAPTURE), Frieda Saeysstraat 1, Gent 9000, Belgium; BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent 9000, Belgium.

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Classifications MeSH