Efficient economic model predictive control of water treatment process with the learning-based Koopman operator
Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities. In this study, we propose a data-driven economic predictive control approach...
| Principais autores: | , , , |
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| Outros Autores: | |
| Formato: | Journal Article |
| Idioma: | English |
| Publicado em: |
2024
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| Assuntos: | |
| Acesso em linha: | https://hdl.handle.net/10356/176190 |
| _version_ | 1826122157451640832 |
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| author | Han, Minghao Yao, Jingshi Law, Adrian Wing-Keung Yin, Xunyuan |
| author2 | School of Chemistry, Chemical Engineering and Biotechnology |
| author_facet | School of Chemistry, Chemical Engineering and Biotechnology Han, Minghao Yao, Jingshi Law, Adrian Wing-Keung Yin, Xunyuan |
| author_sort | Han, Minghao |
| collection | NTU |
| description | Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities.
In this study, we propose a data-driven economic predictive control approach within the Koopman modeling framework.
First, we propose a deep learning-enabled input-output Koopman modeling approach, which predicts the overall economic operational cost of the water treatment processes based on input data and available outputs that are directly linked to the operational costs.
Subsequently, by leveraging this learned input-output Koopman model, a convex economic predictive control scheme is developed. The resulting predictive control problem can be efficiently solved by leveraging quadratic programming solvers, and complex non-convex optimization problems are bypassed. The proposed method is applied to a benchmark water treatment configuration, and the results show that it significantly improves the overall economic operational performance. Additionally, the computational efficiency of the proposed method is significantly enhanced as compared to benchmark control solutions. |
| first_indexed | 2024-10-01T05:43:54Z |
| format | Journal Article |
| id | ntu-10356/176190 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:43:54Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1761902024-07-22T08:46:00Z Efficient economic model predictive control of water treatment process with the learning-based Koopman operator Han, Minghao Yao, Jingshi Law, Adrian Wing-Keung Yin, Xunyuan School of Chemistry, Chemical Engineering and Biotechnology School of Civil and Environmental Engineering Environmental Process Modelling Centre Nanyang Environment and Water Research Institute Computer and Information Science Engineering Economic model predictive control Koopman operator Learning-based modeling and control Water treatment process Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities. In this study, we propose a data-driven economic predictive control approach within the Koopman modeling framework. First, we propose a deep learning-enabled input-output Koopman modeling approach, which predicts the overall economic operational cost of the water treatment processes based on input data and available outputs that are directly linked to the operational costs. Subsequently, by leveraging this learned input-output Koopman model, a convex economic predictive control scheme is developed. The resulting predictive control problem can be efficiently solved by leveraging quadratic programming solvers, and complex non-convex optimization problems are bypassed. The proposed method is applied to a benchmark water treatment configuration, and the results show that it significantly improves the overall economic operational performance. Additionally, the computational efficiency of the proposed method is significantly enhanced as compared to benchmark control solutions. Ministry of Education (MOE) National Research Foundation (NRF) Public Utilities Board (PUB) Submitted/Accepted version This research is supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RG63/22). This research is also supported by the National Research Foundation, Singapore, and PUB, Singapore’s National Water Agency under its RIE2025 Urban Solutions and Sustainability (USS) (Water) Centre of Excellence (CoE) Programme, awarded to Nanyang Environment & Water Research Institute (NEWRI), Nanyang Technological University, Singapore (NTU). 2024-05-31T07:06:18Z 2024-05-31T07:06:18Z 2024 Journal Article Han, M., Yao, J., Law, A. W. & Yin, X. (2024). Efficient economic model predictive control of water treatment process with the learning-based Koopman operator. Control Engineering Practice, 149, 105975-. https://dx.doi.org/10.1016/j.conengprac.2024.105975 0967-0661 https://hdl.handle.net/10356/176190 10.1016/j.conengprac.2024.105975 149 105975 en RG63/22 Control Engineering Practice © 2024 Elsevier Ltd. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1016/j.conengprac.2024.105975. application/pdf |
| spellingShingle | Computer and Information Science Engineering Economic model predictive control Koopman operator Learning-based modeling and control Water treatment process Han, Minghao Yao, Jingshi Law, Adrian Wing-Keung Yin, Xunyuan Efficient economic model predictive control of water treatment process with the learning-based Koopman operator |
| title | Efficient economic model predictive control of water treatment process with the learning-based Koopman operator |
| title_full | Efficient economic model predictive control of water treatment process with the learning-based Koopman operator |
| title_fullStr | Efficient economic model predictive control of water treatment process with the learning-based Koopman operator |
| title_full_unstemmed | Efficient economic model predictive control of water treatment process with the learning-based Koopman operator |
| title_short | Efficient economic model predictive control of water treatment process with the learning-based Koopman operator |
| title_sort | efficient economic model predictive control of water treatment process with the learning based koopman operator |
| topic | Computer and Information Science Engineering Economic model predictive control Koopman operator Learning-based modeling and control Water treatment process |
| url | https://hdl.handle.net/10356/176190 |
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