Prediction of Optimum Dosage of Coagulant in Water Treatment Plant: A Comparative Study between Artificial Neural Network and Random Forest

Abstract

Raw water, sourced directly from natural water bodies, is unsuitable for direct consumption due to the presence of various impurities. Therefore, it undergoes treatment at a Water Treatment Plant (WTP) before being supplied to the public. Preliminary treatment involves the removal of floating matter, through screening, while heavier particles settle out by gravity, fine particles remain in suspension, causing turbidity. Effective removal of these suspended particles requires coagulation to form flocs and facilitate the settling. Determining the optimal coagulant dosage is crucial, as both underdoing and overdosing of coagulant can lead to ineffective treatment and increased costs. Conventionally optimum dosage of coagulant is determined by performing jar test. This study focuses on predicting the optimum coagulant dosage using two soft computing techniques: Artificial Neural Network (ANN) and Random Forest (RF). The Input parameters for model development include turbidity, pH, temperature, and alkalinity of raw water from the Parvati Water Treatment Plant, Pune. In this study Four models were developed, namely Model A (Turbidity), Model B (pH, Alkalinity, Temperature, Turbidity), Model C (pH, Alkalinity, Temperature), and Model D (Alkalinity and Turbidity). These models were trained using ANN and RF. Predictions of optimum coagulant doses were made for the testing dataset, and model accuracy was evaluated using Scatter plots, Root Mean Squared Error (RMSE) and Coefficient of Correlation (R). Results indicate that RMSE values of ANN Models are comparatively lower than RF. Comparing among Models A, B, C, and D, Model B and Model D exhibit better performance, with lower RMSE values.