使用混合表面复合-机器学习模型预测Cr (VI) 和As (V) 在石上的吸附
Kai Chen1, Chuling Guo1, Chaoping Wang1
1School of Environment and Energy, South China University of Technology, Guangzhou 510006, PR China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, PR China.
Water research
|April 13, 2024
概括
一个结合表面复杂化建模和机器学习 (SCM-ML) 的新混合模型准确地预测了石上的酸盐和酸盐吸附. 这种以物种为基础的方法为表面吸附建模提供了更好的准确性和实际应用.
科学领域:
- 环境化学环境化学
- 地质化学 地质化学
- 计算化学计算化学
背景情况:
- 戈伊是从水中吸附氧化离子的关键矿物质,如酸盐和酸盐.
- 对吸附的准确建模对于环境修复至关重要.
- 表面复杂化建模 (SCM) 和机器学习 (ML) 是环境研究的强大工具.
研究的目的:
- 开发和评估一种新的混合表面复杂化建模-机器学习 (SCM-ML) 模型,用于预测石上的染色体和酸盐吸附.
- 为了比较两个SCM-ML方法的性能:参数建模和基于物种的建模.
- 评估开发模型的预测准确性和可解释性.
主要方法:
- 研究了两种SCM-ML混合方法:利用ML优化SCM常数,使用基于物种的ML模型.
- 从未优化的SCM中将表面物种信息纳入ML模型作为输入特征.
- 利用本地可解释的模型不可知解释 (LIME) 来实现模型的可解释性.
主要成果:
- 优化SCM常数的ML方法由于局部极端而失败.
- 基于物种的SCM-ML模型显示了pH边缘,异热和动力数据的卓越预测准确性 (RMSE:5.90%的染料,4.84%的酸盐).
- 由LIME增强的物种信息模型提供了可解释的表面物种信息.
结论:
- 基于物种的SCM-ML混合建模是一种非常实用和准确的方法,用于预测goethite上的oxyanion吸附.
- 这种方法克服了传统的SCM优化与ML的局限性.
- 拟议的方法代表了表面吸附建模中的潜在新范式.
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