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机器学习模型用于预测从污染的土壤中提升表面活性剂的油脂去除
Ehsan Hajibolouri1, Bakbergen Bekbau1, Sagyn Omirbekov2
1Department of Mechanics, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Journal of hazardous materials
|October 22, 2025
概括
机器学习,特别是分类增强,有效地预测土壤修复效率. 这种人工智能驱动的方法优化了表面活性剂增强修复 (SER) 的石油碳化合物清理,降低了风险和成本.
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.
- 数据科学数据科学数据科学
背景情况:
- 土壤被石油碳化合物污染给环境带来了重大风险.
- 表面活性剂增强修复 (SER) 是有效的,但由于非线性相互作用而复杂的模型.
研究的目的:
- 开发和验证用于预测SER效率的机器学习模型.
- 确定影响土壤修复结果的关键因素.
主要方法:
- 使用2394个样本的数据集训练和验证了六个预测模型.
- 采用了分类增强 (CB),极端梯度增强和决策树模型.
- 使用交叉验证和蒙特卡洛灵敏度分析.
主要成果:
- 分类提升 (CB) 模型实现了最高的性能 (R2 = 0.985,RMSE = 0.068).
- 96.4%的CB模型预测属于统计适用性领域.
- 调动速度,表面活性剂度,液体与土壤的比率和洗时间被确定为关键因素.
结论:
- 人工智能驱动的建模,特别是CB,为优化SER设计提供了有效的工具.
- 与传统方法相比,这种方法可以降低运营风险和成本.
- 机器学习提高了有效的土壤清理策略的设计和实施.
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