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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of water process engineering Jg. 77; S. 108316
Hauptverfasser: Ma, Zeyu, Zhu, Yunyi, Chen, Chunsheng, Li, Ting, Li, Yanan, Li, Xiaoding, Wang, Yuan, Waite, T. David, Guan, Jing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2025
Schlagworte:
Artificial intelligence (AI)
Digital twin
Digital water
Internet of things (IoT)
Water and wastewater treatment plants
Water and wastewater treatment plants
Digital twin
Artificial intelligence (AI)
Digital water
Internet of things (IoT)
ISSN:2214-7144, 2214-7144
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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
ISSN:2214-7144
2214-7144
DOI:10.1016/j.jwpe.2025.108316