Image recognition enhances efficient monitoring of the coagulation-settling in drinking water treatment plants

Abstract

Water pollution is a major issue in the context of increasing population and industrialization, yet many drinking water treatment plants (DWTPs) are not fully efficient countering it. In particular, coagulation-settling stage often faces multiple disturbances and time lags, which lower the efficiency because coagulant dosage cannot be accurately calculated in real-time based on the effluent turbidity. To address this issue, we developed a method using deep learning image recognition to monitor the coagulation-settling stage in real-time. For that we used 5761 operational data and images of flocs from the sedimentation tank of a DWTP in East China in 2022, to build an image recognition regression model that predict the turbidity of the sedimentation tank effluent. Results show that our deep learning regression model, performs better with r-square (R²) of 0.97, mean absolute error (MAE) of 0.016 and mean absolute percentage error (MAPE) of 2.74%, compared with the traditional machine learning giving R² of 0.76, MAE of 0.045 and MAPE of 8.26%. The model also avoids misclassification at different turbidity intervals. The incorporation operational data of the sedimentation tank, prediction accuracy is improved by 79.6%. By adjusting the turbidity data to correct time misalignment, our model effectively handles the time lag caused by the hydraulic retention time of the sedimentation tank, thus enhancing the timeliness and accuracy of its practical application.