Hybrid modelling of water resource recovery facilities : status and opportunities
Schneider, Mariane Yvonne ; Quaghebeur, Ward ; Borzooei, Sina ; Froemelt, Andreas ; Li, Feiyi ; Saagi, Ramesh LU
; Wade, Matthew J.
; Zhu, Jun Jie
and Torfs, Elena
(2022)
In Water Science and Technology
85(9).
p.2503-2524
- 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... (More)
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.
(Less)
- author
- Schneider, Mariane Yvonne
; Quaghebeur, Ward
; Borzooei, Sina
; Froemelt, Andreas
; Li, Feiyi
; Saagi, Ramesh
LU
; Wade, Matthew J.
; Zhu, Jun Jie
and Torfs, Elena
- organization
- publishing date
- 2022-05-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- data-driven model, hybrid model, mechanistic model, process control, urban water management, wastewater
- in
- Water Science and Technology
- volume
- 85
- issue
- 9
- pages
- 22 pages
- publisher
- IWA Publishing
- external identifiers
-
- pmid:35576250
- scopus:85130034810
- ISSN
- 0273-1223
- DOI
- 10.2166/wst.2022.115
- language
- English
- LU publication?
- yes
- id
- 33621848-31de-466b-be55-f4c6e874ae38
- date added to LUP
- 2023-01-20 12:59:06
- date last changed
- 2024-04-18 18:17:10
@article{33621848-31de-466b-be55-f4c6e874ae38,
abstract = {{<p>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.</p>}},
author = {{Schneider, Mariane Yvonne and Quaghebeur, Ward and Borzooei, Sina and Froemelt, Andreas and Li, Feiyi and Saagi, Ramesh and Wade, Matthew J. and Zhu, Jun Jie and Torfs, Elena}},
issn = {{0273-1223}},
keywords = {{data-driven model; hybrid model; mechanistic model; process control; urban water management; wastewater}},
language = {{eng}},
month = {{05}},
number = {{9}},
pages = {{2503--2524}},
publisher = {{IWA Publishing}},
series = {{Water Science and Technology}},
title = {{Hybrid modelling of water resource recovery facilities : status and opportunities}},
url = {{http://dx.doi.org/10.2166/wst.2022.115}},
doi = {{10.2166/wst.2022.115}},
volume = {{85}},
year = {{2022}},
}