桥梁古典和智能模型:机器学习引导的吸附异热体,用于在多孔碳上定制的乙捕获
Ali Pourian1, Sina Maghsoudy1, Sherif Farag2
1Surface Reaction and Advanced Energy Materials Laboratory, Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), PO Box 15875-4413, Tehran, Iran.
Environmental science and pollution research international
|October 25, 2025
概括
这项研究引入了一个框架,将吸附异温和机器学习结合起来,以预测碳吸附剂上的乙捕获. 机器学习模型,特别是人工神经网络 (ANN),在高效的挥发性有机化合物 (VOC) 控制方面显著优于经典的异温.
科学领域:
- 环境化学环境化学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 乙是一种主要的挥发性有机化合物 (VOC),具有重大环境和健康风险.
- 对乙的有效捕获策略对于环境保护和公共卫生至关重要.
研究的目的:
- 开发和评估一个整合经典吸附异温和机器学习模型的框架,用于预测碳酸吸附剂上的乙捕获.
- 将机器学习模型的预测性能与传统的同温模型进行比较.
主要方法:
- 一个数据集的分析,包括28个多孔碳的1004个实验点.
- 经典吸附等温的应用:兰慕尔,弗洛因利希和西普斯.
- 机器学习模型的实施:自适应神经模糊推理系统 (ANFIS),人工神经网络 (ANN),决策树 (DT) 和K-最近邻居 (KNN).
主要成果:
- 在经典模型中,Sips 异热法提供了最好的匹配,其平方误差总和 (SSE) 为 99.286.
- 该ANN模型表现出卓越的性能,将SSE降低到大约5.767,几乎是数量级的改进.
- 影响乙吸收的关键因素包括压力,毛孔总体积,BET表面积和含量.
结论:
- 机器学习模型,特别是ANN,与经典异温相比,在乙捕获方面提供了显著更高的预测准确度.
- 拟议的智能框架显示出优化吸附剂性能和开发用于VOC控制的先进碳酸性材料的巨大潜力.
- 这项研究通过有效的VOC管理,有助于提高公共卫生和环境可持续性.
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