Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction  

作　　者：Wiley Helm Shifa Zhong Elliot Reid Thomas Igou Yongsheng Chen 

机构地区：[1]School of Civil and Environmental Engineering,Georgia Institute of Technology,Atlanta,GA 30332,USA [2]School of Ecological and Environmental Sciences,East China Normal University,Shanghai 200241,China

出　　处：《Frontiers of Environmental Science & Engineering》2024年第2期35-46,共12页环境科学与工程前沿（英文）

基　　金：supported by:US Department of Agriculture’s National Institute of Food and Agriculture,Agriculture and Food Research Initiative,Water for Food Production Systems(No.2018-68011-28371);National Science Foundation(USA)(Nos.1936928,2112533);US Department of Agriculture’National Institute of Food and Agriculture(No.2020-67021-31526);US Environmental Protection Agency(No.840080010).

摘　　要：Chlorine-based disinfection is ubiquitous in conventional drinking water treatment (DWT) and serves to mitigate threats of acute microbial disease caused by pathogens that may be present in source water. An important index of disinfection efficiency is the free chlorine residual (FCR), a regulated disinfection parameter in the US that indirectly measures disinfectant power for prevention of microbial recontamination during DWT and distribution. This work demonstrates how machine learning (ML) can be implemented to improve FCR forecasting when supplied with water quality data from a real, full-scale chlorine disinfection system in Georgia, USA. More precisely, a gradient-boosting ML method (CatBoost) was developed from a full year of DWT plant-generated chlorine disinfection data, including water quality parameters (e.g., temperature, turbidity, pH) and operational process data (e.g., flowrates), to predict FCR. Four gradient-boosting models were implemented, with the highest performance achieving a coefficient of determination, R2, of 0.937. Values that provide explanations using Shapley’s additive method were used to interpret the model’s results, uncovering that standard DWT operating parameters, although non-intuitive and theoretically non-causal, vastly improved prediction performance. These results provide a base case for data-driven DWT disinfection supervision and suggest process monitoring methods to provide better information to plant operators for implementation of safe chlorine dosing to maintain optimum FCR.

关 键 词：Machine learning Data-driven modeling Drinking water treatment DISINFECTION CHLORINATION 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]