Neural Network Modelling for Prediction of Coagulant Dosage

Abstract

Water treatment plants (WTP) in Indonesia commonly applied conventional treatment that, among others, relies on the coagulation process to treat turbid surface water to produce clean and safe water for daily purposes. Meanwhile, standard Jar-Test methods for coagulant dosage determination are inefficient, inaccurate, and possibly will not function well for water with high variability of quality. This study aims to improve and optimize the coagulant dosing process by considering the main parameter that greatly affects the coagulation process, i.e., turbidity. For this purpose, a multilayer artificial neural network model (ANN) with an adaptive backpropagation algorithm was developed using MATLAB software. A network architecture with 1 input, 5 hidden and 1 output was implemented. For the input, 4465 turbidity data of the raw water taken from Surabaya Municipal Water Enterprise (PDAM) were used, while the output is the optimal dose obtained from the results of the Jar-test. This study resulted in an empirical model of the optimum dosage of coagulants as a function of the turbidity of the raw water. Training and testing on the given ANN resulted in R2 (determination coefficient) of 0.99444 from regression analysis with the following equation: output = 0.99 x target + 0.66. This study showed that the ANN model provides a very high success rate for coagulant dose prediction.