Toward a Digital Twin for Industrial Wastewater Treatment Plants: A Framework Integrating Mechanistic and Deep Learning Models

Min Yang; Ke Chen; Ning Gui; Yu Tao; Ying Chen; Jinxiang Liu; Bernard De Baets; Aijie Wang

doi:10.1016/j.eng.2026.07.008

Engineering ›› :202607008 DOI: 10.1016/j.eng.2026.07.008

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Toward a Digital Twin for Industrial Wastewater Treatment Plants: A Framework Integrating Mechanistic and Deep Learning Models

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Abstract

The advancement of intelligent wastewater treatment urgently requires a high-accuracy digital-twin framework for real-world applications. However, the widespread prevalence of missing or corrupted sensor data, coupled with process non-linearity and dynamic variability, continues to hinder the reliability of conventional modeling and control approaches in industrial wastewater treatment. To overcome these challenges, this study proposes an end-to-end framework that integrates mechanistic-model-guided data preprocessing, multi-architecture deep learning prediction, and model predictive control for industrial wastewater treatment. Employing real-time sensor data from the Hongxing Pharmaceutical Industrial Park’s wastewater treatment plant (transmitted via the message queuing telemetry transport (MQTT) protocol), the proposed framework was rigorously validated using a complete workflow, which comprised data preprocessing, model prediction, predictive control, and transmitting feedback about the optimized control variables to the plant’s programmable logic controller. When compared with their conventional counterparts, the novel methods implemented in each module of the proposed framework deliver higher data quality and reliability, superior prediction accuracy, and quicker response times for control optimization. This end-to-end framework provides a foundation for future applications of digital-twin technology for intelligent industrial wastewater management.

Keywords

End-to-end / Pharmaceutical industrial wastewater treatment / Deep learning / Mechanistic model / Digital twin

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Min Yang, Ke Chen, Ning Gui, Yu Tao, Ying Chen, Jinxiang Liu, Bernard De Baets, Aijie Wang. Toward a Digital Twin for Industrial Wastewater Treatment Plants: A Framework Integrating Mechanistic and Deep Learning Models. Engineering 202607008 DOI:10.1016/j.eng.2026.07.008

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