Towards the digitalization of water treatment facilities: A case study on machine learning-enabled digital twins
The digital transformation of water treatment facilities through machine learning-enabled Digital Twins (ML-DTs) offers a paradigm shift in sustainable water management. This work introduces an embedded DT framework that seamlessly integrates water treatment facility equipment, middleware, cloud com...
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| Veröffentlicht in: | Journal of water process engineering Jg. 77; S. 108316 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.09.2025
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| ISSN: | 2214-7144, 2214-7144 |
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| Abstract | The digital transformation of water treatment facilities through machine learning-enabled Digital Twins (ML-DTs) offers a paradigm shift in sustainable water management. This work introduces an embedded DT framework that seamlessly integrates water treatment facility equipment, middleware, cloud computing, and predictive analytics. A case study on centralized membrane bioreactor (MBR) wastewater treatment plants demonstrates ML-DTs' capability for proactive process control and maintenance (PC&M): specifically, a knowledge-based multi-objective particle swarm optimization (KBMOPSO) fuzzy controller reducing aeration energy consumption of the aerobic zone from 0.12 to 0.15 kWh/t to 0.06–0.12 kWh/t, while maintaining required effluent quality. In parallel, a long short-term memory (LSTM) encoder-decoder model achieved accurate forecasting of MBR membrane fouling (MAPE < 6.45 %, R2 > 0.87), enabling operators to proactively determine the need for online chemical cleaning under dynamic operating conditions. Despite these promising outcomes, the broader adoption of ML-DTs faces several barriers, including limited data availability, technical integration challenges, and organizational and human resource constraints. This work also provides actionable insights to help facilitate the transition towards intelligent water treatment facilities through the implementation of ML-DTs.
•An embedded framework was proposed for Digital Twins of water treatment facilities•Different remote access options for water treatment systems were evaluated•A case study on ML-DTs of full-scale MBR WWTPs were reviewed•ML-DTs enabled proactive PC&M using both historical and real-time data |
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| AbstractList | The digital transformation of water treatment facilities through machine learning-enabled Digital Twins (ML-DTs) offers a paradigm shift in sustainable water management. This work introduces an embedded DT framework that seamlessly integrates water treatment facility equipment, middleware, cloud computing, and predictive analytics. A case study on centralized membrane bioreactor (MBR) wastewater treatment plants demonstrates ML-DTs' capability for proactive process control and maintenance (PC&M): specifically, a knowledge-based multi-objective particle swarm optimization (KBMOPSO) fuzzy controller reducing aeration energy consumption of the aerobic zone from 0.12 to 0.15 kWh/t to 0.06–0.12 kWh/t, while maintaining required effluent quality. In parallel, a long short-term memory (LSTM) encoder-decoder model achieved accurate forecasting of MBR membrane fouling (MAPE < 6.45 %, R2 > 0.87), enabling operators to proactively determine the need for online chemical cleaning under dynamic operating conditions. Despite these promising outcomes, the broader adoption of ML-DTs faces several barriers, including limited data availability, technical integration challenges, and organizational and human resource constraints. This work also provides actionable insights to help facilitate the transition towards intelligent water treatment facilities through the implementation of ML-DTs.
•An embedded framework was proposed for Digital Twins of water treatment facilities•Different remote access options for water treatment systems were evaluated•A case study on ML-DTs of full-scale MBR WWTPs were reviewed•ML-DTs enabled proactive PC&M using both historical and real-time data |
| ArticleNumber | 108316 |
| Author | Li, Xiaoding Zhu, Yunyi Waite, T. David Ma, Zeyu Chen, Chunsheng Li, Ting Guan, Jing Wang, Yuan Li, Yanan |
| Author_xml | – sequence: 1 givenname: Zeyu surname: Ma fullname: Ma, Zeyu organization: Beijing OriginWater Technology Co., Ltd., Beijing, China – sequence: 2 givenname: Yunyi surname: Zhu fullname: Zhu, Yunyi organization: UNSW Centre for Transformational Environmental Technologies (CTET), Yixing, China – sequence: 3 givenname: Chunsheng surname: Chen fullname: Chen, Chunsheng organization: Beijing OriginWater Technology Co., Ltd., Beijing, China – sequence: 4 givenname: Ting surname: Li fullname: Li, Ting organization: Beijing OriginWater Technology Co., Ltd., Beijing, China – sequence: 5 givenname: Yanan surname: Li fullname: Li, Yanan organization: Beijing OriginWater Technology Co., Ltd., Beijing, China – sequence: 6 givenname: Xiaoding surname: Li fullname: Li, Xiaoding organization: Beijing OriginWater Technology Co., Ltd., Beijing, China – sequence: 7 givenname: Yuan surname: Wang fullname: Wang, Yuan organization: UNSW Centre for Transformational Environmental Technologies (CTET), Yixing, China – sequence: 8 givenname: T. David surname: Waite fullname: Waite, T. David organization: UNSW Centre for Transformational Environmental Technologies (CTET), Yixing, China – sequence: 9 givenname: Jing surname: Guan fullname: Guan, Jing email: Jing.guan@originwaterinternational.com organization: Beijing OriginWater Technology Co., Ltd., Beijing, China |
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| Cites_doi | 10.1016/j.eng.2024.04.012 10.1016/j.cirp.2018.04.055 10.1016/j.jup.2014.12.006 10.1021/acs.est.9b04251 10.1016/j.jwpe.2024.105523 10.1007/s11783-023-1735-8 10.1007/s00253-003-1384-6 10.3390/w14091384 10.1002/(SICI)1097-0290(19970120)53:2<168::AID-BIT6>3.0.CO;2-M 10.1016/j.jwpe.2022.102974 10.1007/s11783-013-0623-z 10.1007/s11783-023-1752-7 10.1080/01944363.2014.935673 10.1177/0954405420970517 10.1007/s11783-025-1954-2 10.1016/j.desal.2023.116647 10.1109/TII.2018.2873186 10.1007/s11783-024-1780-y 10.1007/s11783-024-1832-3 10.1038/s43588-024-00603-w 10.1016/j.jhydrol.2024.131808 |
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| Keywords | Water and wastewater treatment plants Digital twin Artificial intelligence (AI) Digital water Internet of things (IoT) |
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| SubjectTerms | Artificial intelligence (AI) Digital twin Digital water Internet of things (IoT) Water and wastewater treatment plants |
| Title | Towards the digitalization of water treatment facilities: A case study on machine learning-enabled digital twins |
| URI | https://dx.doi.org/10.1016/j.jwpe.2025.108316 |
| Volume | 77 |
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