Hybrid modelling of water resource recovery facilities: status and opportunities. (6th April 2022)

• Record Type: Journal Article
• Authors: Schneider, Mariane Yvonne; Quaghebeur, Ward; Borzooei, Sina; Froemelt, Andreas; Li, Feiyi; Saagi, Ramesh; Wade, Matthew J.; Zhu, Jun-Jie; Torfs, Elena
• Is Part Of: Water science and technology. Volume 85:Number 9(2022)
• Page Start: 2503
• Page End: 2524
• Publication Date: 2022-04-06
• Subjects: data-driven model -- hybrid model -- mechanistic model -- process control -- urban water management -- wastewater; Water -- Pollution; Sewage -- Purification; Water quality management; Periodicals; 628.168
• DOI: 10.2166/wst.2022.115
• Languages: English
• ISSNs: 0273-1223

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. HIGHLIGHTS: HM combines mechanistic and data-driven models. WRRFs are too sensitive to rely only on data-driven techniques; HM is an alternative. Hybrid models have a high predictive power at low computational cost, proving them useful for online optimisation and control. Major challenges for more widespread implementation of HM are discussed. HM supports the transition towards a digital and resource-efficient water sector. Graphical Abstract