Prediction of the Optimal Dosage of Poly Aluminum Chloride for Coagulation in Drinking Water Treatment using Artificial Neural Networks

Abstract

Drinking–water Treatment Plants (DWTP) dosing coagulant chemicals determines the success of water quality. The addition of these compounds is usually a manual procedure performed by trained people. This task is quite difficult because it requires a lot of experience for a correct dosage. To solve this problem, this study is based on the analysis of data collected from a raw water source located in Ecuador. Then, using the information on the physical-chemical parameters of the raw water, the definition of the doses of Polyaluminum Chloride (PAC), and the input and output variables of the dosage process are identified. Consequently, the implementation of an intelligent control system based on Artificial Neural Networks (ANN) is proposed. These experiments start with data collection and analysis in order to establish the variables involved in the process. The proposed neural model has three hidden layers, and it uses adaptive gradient algorithms. An analysis of the results was performed using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The PAC predictive model in the training phase gives a MAPE value of 0.0425 for the not adjusted values and 0.0262 for the adjusted numerical values. However, in the test phase the neural model achieves a MAPE of 0.057 for the not adjusted PAC values and 0.041 for the adjusted values. It can be concluded that this alternative provides an efficient solution when solving dosing problems in DWTPs, having reliable results from the RMSE and MAPE metrics.