Comparing artificial and deep neural network models for prediction of coagulant amount and settled water turbidity: Lessons learned from big data in water treatment operations

Abstract

Machine learning has been applied to the modeling of water treatment processes. While machine learning models have a great ability to handle nonlinear relationships in the process, changes in raw water quality and process operations can make predictions difficult. This study investigated the use of machine learning models, including traditional and deep learning approaches, for predicting both coagulant dosage and settled water turbidity in the water treatment process using six years of operating data. The study found that deep learning models, which process temporal sequential data, significantly improved prediction accuracies in response to changing dynamics of water treatment processes. The results emphasize the importance of collecting large datasets for modeling water treatment processes to capture rapid changes in raw water quality, thereby increasing prediction accuracies. The modeling results provide suggestions for model selection, data collection, and monitoring implementation in water treatment plants, which can enhance the accuracy of predictions and ensure high-quality treated water.