应用堆叠机器学习模型来指导抗生素的电化学氧化:关键参数识别和过程优化洞察力
Shichang Jia1, Min Deng1, Yue Mu2
1Chongqing Key Laboratory of Agricultural Resources and Environment, College of Resources and Environment, Southwest University, Chongqing, 400715, PR China.
Journal of environmental management
|August 1, 2025
概括
机器学习模型预测了电化学高级氧化过程 (EAOP) 中抗生素降解效率. 分析了降解条件,抗生素特性和电极特性,以确定有效控制污染的关键因素.
科学领域:
- 环境科学 环境科学
- 电化学 电化学 电化学
- 机器学习 机器学习
背景情况:
- 环境中抗生素的积累是一个重大的全球性问题.
- 电化学高级氧化过程 (EAOPs) 对抗生素降解有希望,但需要优化.
- 确定影响EAOP抗生素去除效果的关键因素仍然是一个挑战.
研究的目的:
- 使用机器学习算法模拟和预测抗生素降解效率.
- 在EAOP中识别和排列影响抗生素去除的关键因素.
- 开发一种用于优化抗生素污染控制战略的预测工具.
主要方法:
- 六个机器学习算法 (GBDT,XGBoost,SVM,KNN,RF,BPNN) 应用于抗生素降解的数据集.
- 数据集包括电极特性,降解条件和抗生素特性 (pKa,logKow).
- 用特征重要性分析和夏普利添加式解释 (SHAP) 来确定关键因素.
主要成果:
- 渐变增强决策树 (GBDT) 模型显示出高预测准确度 (R2 = 0.91).
- 降解条件是影响最大的因素 (69.96%),其次是抗生素特性 (15.4%) 和电极特性 (14.6%).
- 电流密度,抗生素度和pKa被SHAP分析确定为关键因素.
结论:
- 机器学习有效地预测EAOP中的抗生素降解,有助于过程优化.
- 降解条件,特别是电流密度和抗生素度,对于有效的去除至关重要.
- 开发的模型和见解可以指导实验设计,以加强抗生素污染控制.
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