Development of water quality prediction model for water treatment plant using artificial intelligence algorithms
Hwisu Shin, Yong‐Hoon Byun, Sang-Wook Kang, Hitae Shim, S. Oak, Youngsuk Ryu, Hansoo Kim, Nahmchung Jung
Abstract
Accurate prediction of water quality changes in the water treatment process is an important factor for optimal decision-making process such as the design, operation, and diagnosis of water treatment facilities. This study developed an Artificial Intelligence (AI) algorithm model predicting dissolved organic carbon (DOC) removal and disinfection byproducts formation, and comparatively analyzed existing empirical models and prediction results to examine the applicability of AI algorithm techniques to water quality prediction in the water treatment process. We enhanced empirical models for predicting DOC removal and disinfection byproduct formation in Korea water purification plant. Six AI algorithm techniques were applied and tested using real-world data. All AI algorithm models outperformed the original empirical models in predicting DOC removal and byproduct formation. In terms of the DOC prediction model, multi-layer perceptron (MLP) showed the optimal performance (R2 = 0.9795; root mean square error [RMSE] = 0.0365 mg/L). MLP also showed the optimal performance in disinfection byproduct formation prediction (R2 = 0.9781; RMSE = 0.0008 mg/L. As a result, the prediction performance of AI algorithm models improved with larger sample sizes. By securing data samples for approximately 1 year, these models were confirmed to outperform empirical models.