使用集体学习算法预测有机农药被生物炭吸附
Amrita Nighojkar1,2, Jyoti Nagpal3, Winston Soboyejo4
1Department of Metallurgical and Materials Engineering, Defence Institute of Advanced Technology (DU), Pune, India. nighojkar.amrita@gmail.com.
Environmental monitoring and assessment
|July 24, 2023
概括
机器学习准确地预测了使用生物炭从水中去除农药的情况. 一个XGBoost模型有效地预测了有机吸附,有助于开发环保的水处理材料.
科学领域:
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
- 计算化学计算化学
背景情况:
- 机器学习 (ML) 模型对于环保意识的材料科学研究越来越重要.
- 使用生态材料预测废水处理效率对于应对新出现的污染物至关重要.
研究的目的:
- 模拟从水系统中吸附有机酸盐农药的模式,使用基于决策树的高级集体学习算法.
- 确定实验因素与农药吸附效率在生物炭上的关系.
主要方法:
- 使用实验室生物炭吸附数据开发和训练了一种基于XGBoost的集合ML模型.
- 输入参数包括热解温度,pH值,表面积,孔隙体积和初始杀虫剂度.
- 在模型训练中使用了Python中的网格搜索优化.
主要成果:
- 在XGBoost模型实现高预测准确度 (R2train = 0.998,R2test = 0.981).
- 有机化合物度和生物炭形态显著影响了吸附.
- 集体学习框架显示出强大的预测能力,用于农药吸附.
结论:
- 集体学习算法显示了设计有效的碳丰富生物材料以去除微污染物的巨大潜力.
- 这种方法有助于开发用于废水处理的先进生态材料.
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