通过使用各种训练算法,通过深度神经网络研究纹理性质对通过多孔碳中CO2吸附的影响
Pardis Mehrmohammadi1, Ahad Ghaemi2
1School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, 16765-193, Iran.
Scientific reports
|December 1, 2023
概括
这项研究使用多层感知子 (MLP) 神经网络来模拟二氧化碳 (CO2) 在多孔碳体上的吸附. 莱文伯格-马奎特 (LM) 算法在预测二氧化碳捕获方面表现出卓越的准确性和效率.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 在多孔碳材料上吸附二氧化碳 (CO2) 是减排的关键策略.
- 了解纹理特性的影响,特别是微孔,对于优化二氧化碳捕获能力至关重要.
研究的目的:
- 用多层感知器 (MLP) 神经网络研究纹理特性对二氧化碳吸附能力的影响.
- 为了评估模拟二氧化碳吸附的不同训练算法的性能.
- 为了确定最佳的神经网络配置,准确预测二氧化碳吸附行为.
主要方法:
- 多层感知子 (MLP) 神经网络使用各种算法进行训练,包括Levenberg-Marquardt (LM),缩放合梯度 (SCG) 和Broyden-Fletcher-Goldfarb-Shanno (BFGS).
- 辐射基函数 (RBF) 网络被优化为配置.
- 性能是根据平均平方误差 (MSE),运行时间和趋同时代进行评估的.
主要成果:
- 莱文伯格-马奎特 (LM) 算法实现了最高的准确性,平均平方误差 (MSE) 为2.6293E-5.
- 缩放联梯度 (SCG) 算法提供了最短的运行时间,而BFGS是最慢的.
- 一个最优的RBF网络配置与九个隐藏的神经元产生了一个MSE的9.840E-5.
- 使用LM和贝叶斯规范化 (BR) 算法训练的MLP网络在新数据上显示了最高的准确性.
结论:
- 对于模拟二氧化碳吸附过程,MLP深度神经网络,特别是LM和BR训练算法,是有效的.
- 这项研究为二氧化碳吸附的压力依赖性行为提供了宝贵的见解,突出了微孔和中孔的作用.
- 这些发现增强了用于二氧化碳捕获应用的多孔材料中气体吸附行为的预测.
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