Constructing a visual detection method for coagulation effect based on image feature machine learning

The coagulation process is influenced by several factors, including turbidity, temperature, pH, and hydraulic conditions. Consequently, optimizing the dosage of coagulants is often time-consuming and challenging. To quickly evaluate coagulation efficacy and optimize coagulant dosage, this study utilized a convolutional neural network (CNN) model to analyze the accuracy of effluent turbidity predictions from floc images at different time intervals. Python OpenCV was employed to develop a program that extracts macro features (e.g., texture) and micro features (e.g., particle size distribution) from the images, and these feature parameters were analyzed using a BP-ANN model. The results of the CNN analysis indicated that the middle to late stages of flocculation are the optimal periods for predicting effluent turbidity, with the highest prediction accuracy of 99.81 % achieved during the middle stage of flocculation. The BP-ANN analysis demonstrated that particle size distribution effectively represents the microscopic characteristics of flocs, achieving a maximum prediction accuracy of 96.94 % in the middle stage of flocculation. Furthermore, combining macroscopic and microscopic features yielded a prediction accuracy of 99.44 % at the end of flocculation using BP-ANN. These findings suggest that machine learning techniques applied to floc images can effectively predict effluent turbidity, offering valuable insights for future water quality prediction and the development of flocculation kinetics models.