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

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外文摘要：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.

外文关键词：

Machine learningData-driven modelingDrinking water treatmentDisinfectionChlorination

作者：

Wiley Helm、Shifa Zhong、Elliot Reid、Thomas Igou、Yongsheng Chen

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作者单位：

School of Civil and Environmental Engineering,Georgia Institute of Technology,Atlanta,GA 30332,USA

School of Ecological and Environmental Sciences,East China Normal University,Shanghai 200241,China

基金：

US Department of Agriculture's National Institute of Food and Agriculture,Agriculture and Food Research Initiative,Water for FooNational Science Foundation(USA)National Science Foundation(USA)US Department of Agriculture'National Institute of Food and AgricultureUS Environmental Protection Agency

项目编号：

2018-68011-28371193692821125332020-67021-31526840080010

出版年：

2024

DOI：

10.1007/s11783-024-1777-6

环境科学与工程前沿

高等教育出版社

环境科学与工程前沿

影响因子：0.545

ISSN：2095-2201

年,卷(期)：2024.18(2)

参考文献量42