Hybrid modelling of water resource recovery facilities: status and opportunities-Reference-Cited by-同舟云学术

Hybrid modelling of water resource recovery facilities: status and opportunities

Published:2022-04-06 Issue:9 Volume:85 Page:2503-2524
ISSN:0273-1223
Container-title:Water Science and Technology
language:en
Short-container-title:

Author:

Schneider Mariane Yvonne¹^ORCID,Quaghebeur Ward²³⁴^ORCID,Borzooei Sina²³^ORCID,Froemelt Andreas⁵^ORCID,Li Feiyi⁶^ORCID,Saagi Ramesh⁷^ORCID,Wade Matthew J.⁸^ORCID,Zhu Jun-Jie⁹^ORCID,Torfs Elena²³^ORCID

Affiliation:

1. a 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

2. b Centre for Advanced Process Technology for Urban Resource recovery (CAPTURE), Frieda Saeysstraat 1, Gent 9000, Belgium

3. c BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent 9000, Belgium

4. d KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, Ghent 9000, Belgium

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

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

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

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

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

Abstract

Abstract 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.

Publisher

IWA Publishing

Subject

Water Science and Technology,Environmental Engineering

Link

https://iwaponline.com/wst/article-pdf/85/9/2503/1064960/wst085092503.pdf

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