机器学习模型用于预测有机污染物的声化学降解速率常数
Iseul Na1, Taeho Kim2, Pengpeng Qiu3
1Department of Environmental Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea; Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Ultrasonics sonochemistry
|August 23, 2024
概括
机器学习模型使用声化学准确预测水中的污染物降解率. XGBoost 模型表现出最高的准确性,确定功率密度和频率是影响污染物去除的关键因素.
科学领域:
- 环境化学环境化学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 声化学降解是一种有前途的先进氧化工艺,用于去除水性有机污染物.
- 准确预测降解动力学对于优化处理效率和反应堆设计至关重要.
- 现有的模型往往缺乏捕捉各种操作和污染物参数的复杂相互作用的能力.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测水性有机污染物的声化学降解中的伪第一阶反应速率常数.
- 确定影响降解速度最有影响的参数.
- 用一个全面的数据集来评估不同ML算法的预测性能.
主要方法:
- 编制了来自89项研究的618项观察数据集,包括超声波,溶液和污染物特征.
- 选择了八个输入变量 (频率,功率密度,pH,温度,初始度,可溶性,蒸汽压力,K) 和降解速率常数作为目标变量.
- 训练和评估随机森林 (RF),极端梯度增强 (XGB) 和轻梯度增强机 (LGB) 模型,使用数据预处理技术,包括异常值删除和数据增强.
主要成果:
- 机器学习模型,特别是XGBoost,在预测伪第一阶降解率常数方面表现出高准确性.
- 随着异常值的删除和数据增强,预测性能显著提高.
- 使用SHAP值,功率密度和频率被确定为最有影响力的输入变量.
结论:
- 开发的ML模型,特别是XGBoost,是预测水性污染物的声化学降解速率的有效工具.
- 这些发现提供了对控制声化学处理效率的关键因素的宝贵见解.
- 该研究强调了ML在优化环境修复中先进的氧化过程方面的潜力.
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