深度学习模型用于预测CO2在基于伊米达的离子液体中的溶解度
Amir Hossein Sheikhshoaei1, Ali Sanati2, Ali Khoshsima1
1Faculty of Petroleum and Chemical Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Scientific reports
|July 21, 2025
概括
深度学习模型可以准确地预测二氧化碳在离子液体中的可溶性. 增长网络模型表现最好,优于传统的SAFT模型,并将压力确定为关键影响因素.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
背景情况:
- 准确预测二氧化碳在离子液体中的可溶性对于碳捕获技术至关重要.
- 基于伊米达的离子液体是有前途的溶剂,但它们的热力学特性需要强大的预测模型.
研究的目的:
- 开发和比较深度学习模型,用于预测二氧化碳在基于伊米达的离子液体中的溶解度.
- 评估各种机器学习算法的性能与已建立的物理模型对比.
主要方法:
- 利用深度学习模型,包括贝叶斯神经网络 (BNN),深度神经网络 (DNN),梯度增强神经网络 (GrowNet),表格神经网络 (TabNet),随机森林 (RF) 和支持向量回归 (SVR).
- 输入参数包括临界压力,临界温度,分子量和离心因子.
- 与两个PC-SAFT模型 (cQC-PC-SAFT-MSA (1) 和cQC-PC-SAFT-MSA (2)) 进行模型性能比较.
主要成果:
- 与PC-SAFT模型相比,深度学习模型显示出更高的性能.
- GrowNet模型实现了最小的误差,根平均平方误差 (RMSE) 为0.0073和确定系数 (R2) 为0.9962.
- 沙普利添加剂描述 (SHAP) 和皮尔森相关系数 (PCC) 分析确定压力 (P) 是影响二氧化碳溶解度的最重要的参数.
结论:
- 深度学习,特别是GrowNet模型,提供了一种非常准确的方法来预测二氧化碳在基于伊米达的离子液体中的溶解度.
- 了解压力等特定参数的影响对于优化使用离子液体的二氧化碳捕获过程至关重要.
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