过程(计算)
计算机科学
机器学习
变量(数学)
人工智能
生化工程
工程类
数学
数学分析
操作系统
作者
Hanyong Bao,Wanxin Yin,Hongcheng Wang,Yin Li,Shijie Jiang,Fidelis Odedishemi Ajibade,Qinghua Ouyang,Yongji Wang,Shichen Nie,Y. Bai,Huiliang Gao,Aijie Wang
标识
DOI:10.1016/j.biortech.2023.129436
摘要
Machine learning models can improve antibiotic removal performance in constructed wetlands (CWs) by optimizing the operation process. However, robust modeling approaches for revealing the complex biochemical treatment process of antibiotics in CWs are still lacking. In this study, two automated machine learning (AutoML) models achieved good performance with different sizes of the training dataset (mean absolute error = 9.94-13.68, coefficient of determination = 0.780-0.877), demonstrating the ability to predict antibiotic removal performance without human intervention. Explainable analysis results (the variable importance and Shapley additive explanations) revealed that the variable substrate type was more influential than the variables of influent wastewater quality and plant type. This study proposed a potential approach to comprehensively understanding the complex effects of key operational variables on antibiotic removal, which serve as a reference for optimizing operational adjustments in the CW process.
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