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基于遗传编程的预测模型,用于污染土壤中现场电动力学修复的Cr去除效应

Qiu Yu¹, Yi Zheng¹, Pengpeng Zhang¹

¹State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China.

Journal of hazardous materials

|September 2, 2023

概括

这项研究使用遗传编程来预测电动力学土壤修复中的去除. 导电率,pH值和电流等关键因素显著影响水平,指导更有效的清洁策略.

科学领域:

环境科学环境科学
化学工程是化学工程的重要组成部分.
土壤科学土壤科学

背景情况:

电动力学修复是一种有效的现场技术,可降低土壤风险.
量化因素对 (Cr) 修复效率的影响具有挑战性.
机器学习为污染物迁移模型提供了潜力,但导出预测公式是复杂的.

研究的目的:

使用遗传编程 (GP) 建立一个用于Cr污染土壤的电动修复的预测模型.
描述模型的自适应优化,并确定关键的灵敏度因素.
为在现场的土壤电动处理提供决策支持.

主要方法:

确定影响Cr污染土壤电动修复的特征参数.
采用基因编程 (GP) 来建立Cr去除的预测模型.
利用Sobol灵敏度分析来确定影响Cr.含量的关键因素.

主要成果:

医师模型准确地预测了Cr (VI) 和总Cr度,准确度很高 (92%的培训,90%的验证数据在1%的误差范围内).
模型有效地捕获了不同土壤层的Cr离子变化.
导电性,pH值,电流和水分含量被确定为影响Cr.水平的重要因素.

结论:

系统电流和土壤pH值是对Cr (VI) 和整个受污染区域的总Cr含量最敏感的因素.
专注于当前和土壤pH值对于优化整治工作至关重要.
全科医生和灵敏度分析为预测补救效应和指导现场电动治疗提供了有价值的工具.

关键词:

电动力学修复电动力学修复这是基因编程.预测模型是一个预测模型.灵敏度分析是一种灵敏度分析.土壤污染土壤污染

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