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

IF 6.1 2区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL Frontiers of Environmental Science & Engineering Pub Date : 2023-09-28 DOI:10.1007/s11783-024-1777-6
Wiley Helm, Shifa Zhong, Elliot Reid, Thomas Igou, Yongsheng Chen
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Abstract

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.

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梯度增强辅助机器学习数据驱动的游离氯残留预测模型的开发
氯基消毒在传统饮用水处理(DWT)中普遍存在,用于减轻由源水中可能存在的病原体引起的急性微生物疾病的威胁。消毒效率的一个重要指标是游离氯残留量(FCR),这是美国规定的消毒参数,间接衡量在DWT和分布过程中防止微生物再污染的消毒能力。这项工作展示了机器学习(ML)如何在提供来自美国乔治亚州一个真实的、全面的氯消毒系统的水质数据时,可以实施来改进FCR预测。更准确地说,根据DWT工厂产生的一整年氯消毒数据,包括水质参数(如温度、浊度、pH值)和操作过程数据(如流量),开发了一种梯度增强ML方法(CatBoost),以预测FCR。采用4种梯度增强模型,其决定系数R2为0.937,效果最佳。使用Shapley的加性方法提供解释的值被用来解释模型的结果,发现标准DWT操作参数虽然不是直观的,理论上也没有因果关系,但极大地提高了预测性能。这些结果为数据驱动的DWT消毒监督提供了基础案例,并提出了过程监测方法,为工厂操作员提供更好的信息,以实施安全的氯剂量,以保持最佳的FCR。
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来源期刊
Frontiers of Environmental Science & Engineering
Frontiers of Environmental Science & Engineering ENGINEERING, ENVIRONMENTAL-ENVIRONMENTAL SCIENCES
CiteScore
10.90
自引率
12.50%
发文量
988
审稿时长
6.1 months
期刊介绍: Frontiers of Environmental Science & Engineering (FESE) is an international journal for researchers interested in a wide range of environmental disciplines. The journal''s aim is to advance and disseminate knowledge in all main branches of environmental science & engineering. The journal emphasizes papers in developing fields, as well as papers showing the interaction between environmental disciplines and other disciplines. FESE is a bi-monthly journal. Its peer-reviewed contents consist of a broad blend of reviews, research papers, policy analyses, short communications, and opinions. Nonscheduled “special issue” and "hot topic", including a review article followed by a couple of related research articles, are organized to publish novel contributions and breaking results on all aspects of environmental field.
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