Hybrid modelling of water resource recovery facilities : status and opportunities
- Author
- Mariane Yvonne Schneider, Ward Quaghebeur, Sina Borzooei (UGent) , Andreas Froemelt, Feiyi Li, Ramesh Saagi, Matthew J. Wade, Jun-Jie Zhu and Elena Torfs (UGent)
- Organization
- 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 WRRF 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.
- Keywords
- data-driven model, hybrid model, mechanistic model, process control, urban water management, wastewater, ARTIFICIAL NEURAL-NETWORKS, ACTIVATED-SLUDGE PROCESS, REAL-TIME CONTROL, TREATMENT PLANTS, 1ST-PRINCIPLES MODELS, PROCESS SIMULATION, PREDICTIVE CONTROL, ANOMALY DETECTION, COAGULANT DOSAGE, BLACK-BOX
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8769293
- MLA
- Schneider, Mariane Yvonne, et al. “Hybrid Modelling of Water Resource Recovery Facilities : Status and Opportunities.” WATER SCIENCE AND TECHNOLOGY, vol. 85, no. 9, 2022, pp. 2503–24, doi:10.2166/wst.2022.115.
- APA
- Schneider, M. Y., Quaghebeur, W., Borzooei, S., Froemelt, A., Li, F., Saagi, R., … Torfs, E. (2022). Hybrid modelling of water resource recovery facilities : status and opportunities. WATER SCIENCE AND TECHNOLOGY, 85(9), 2503–2524. https://doi.org/10.2166/wst.2022.115
- Chicago author-date
- Schneider, Mariane Yvonne, Ward Quaghebeur, Sina Borzooei, Andreas Froemelt, Feiyi Li, Ramesh Saagi, Matthew J. Wade, Jun-Jie Zhu, and Elena Torfs. 2022. “Hybrid Modelling of Water Resource Recovery Facilities : Status and Opportunities.” WATER SCIENCE AND TECHNOLOGY 85 (9): 2503–24. https://doi.org/10.2166/wst.2022.115.
- Chicago author-date (all authors)
- Schneider, Mariane Yvonne, Ward Quaghebeur, Sina Borzooei, Andreas Froemelt, Feiyi Li, Ramesh Saagi, Matthew J. Wade, Jun-Jie Zhu, and Elena Torfs. 2022. “Hybrid Modelling of Water Resource Recovery Facilities : Status and Opportunities.” WATER SCIENCE AND TECHNOLOGY 85 (9): 2503–2524. doi:10.2166/wst.2022.115.
- Vancouver
- 1.Schneider MY, Quaghebeur W, Borzooei S, Froemelt A, Li F, Saagi R, et al. Hybrid modelling of water resource recovery facilities : status and opportunities. WATER SCIENCE AND TECHNOLOGY. 2022;85(9):2503–24.
- IEEE
- [1]M. Y. Schneider et al., “Hybrid modelling of water resource recovery facilities : status and opportunities,” WATER SCIENCE AND TECHNOLOGY, vol. 85, no. 9, pp. 2503–2524, 2022.
@article{8769293,
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 WRRF 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.}},
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}},
journal = {{WATER SCIENCE AND TECHNOLOGY}},
keywords = {{data-driven model,hybrid model,mechanistic model,process control,urban water management,wastewater,ARTIFICIAL NEURAL-NETWORKS,ACTIVATED-SLUDGE PROCESS,REAL-TIME CONTROL,TREATMENT PLANTS,1ST-PRINCIPLES MODELS,PROCESS SIMULATION,PREDICTIVE CONTROL,ANOMALY DETECTION,COAGULANT DOSAGE,BLACK-BOX}},
language = {{eng}},
number = {{9}},
pages = {{2503--2524}},
title = {{Hybrid modelling of water resource recovery facilities : status and opportunities}},
url = {{http://doi.org/10.2166/wst.2022.115}},
volume = {{85}},
year = {{2022}},
}
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