Simultaneous sensor fault diagnosis and reconstruction for intelligent monitoring in wastewater treatment plants: An explainable deep multi-task learning model

Abstract

Sensor malfunctions in wastewater treatment plants (WWTPs) significantly disrupt process control and energy usage, highlighting the critical need for effective sensor fault diagnosis and reconstruction. This study aims to introduce a novel application of a multi-task learning network in WWTPs to address the challenge of sensor malfunction by enabling simultaneous fault diagnosis and reconstruction. The proposed approach introduces an explainable deep multi-task learning autoencoder network (DMTL-UNet), which effectively allows sharing information among tasks through attention gates and residual connections. The effectiveness of the DMTL-UNet model is validated using a real-world dataset from a WWTP in South Korea. The results demonstrate the remarkable capability of the DMTL-UNet model in accurately diagnosing multiple faults (F1-score = 99.08 %) and achieving superior reconstruction performance (RMSE = 31.1175 mg/L) for faulty WWTP sensors. Moreover, implementing the DMTL-UNet model offers significant energy savings of 37.44 %, corresponding to a reduction in the aeriation cost by 1154.91 USD. Therefore, implementing the DMTL-UNet model for calibrating faulty sensors can enhance sensor reliability, improve maintenance practices, and contribute to the sustainable operation of WWTPs.