Prediction of Residual Aluminum Concentration and Size in Water Plants of Chinese Water Transfer Project Through Machine Learning

Abstract

Effluent water in Chinese water transfer project is complicated, leading to possibility of excessive residual aluminum if the coagulant dosage is improper. There exists great risk for controlling residual aluminum based on experiences, while using machine learning can offer appropriate dosage based on the effluent water parameters.  Much attention has been paid to the aluminum below 0.45 μm while for water plant with sand filter only, aluminum over 0.45 μm can still be remnant in the effluent water. In this study, machine learning algorithms were applied for the prediction of pH, aluminum concentration and size after coagulation based on the water parameters and ESI-TOF-MS results of the coagulant.  The results showed that conventional neural network (CNN) showed best prediction ability compared with BP network, random tree and support vector machine, whose R2 was 0.936. The predicted results showed that the aluminum size decreased as the basicity increased at the same dosage, the concentration of aluminum between 20-30μm dropped from 65% to 24%.

Keywords: coagulation, residual aluminum, machine learning, ESI-TOF-MS