使用机器学习和响应表面方法,对二氧化碳吸收到水性类胺的建模
Hadiseh Masoumi1, Ali Imani1, Azam Aslani2
1School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, 13114-16846, Iran.
Scientific reports
|October 13, 2024
概括
这项研究使用先进的计算方法模拟了甲胺溶剂中的二氧化碳 (CO2) 吸收. 多层感知子 (MLP) 和辐射基函数 (RBF) 网络准确预测了CO2质量流量.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 过程建模过程建模
背景情况:
- 阿尔卡诺胺溶剂对于从工业排放中捕获二氧化碳 (CO2) 是至关重要的.
- 对二氧化碳吸收的准确建模对于优化捕获过程至关重要.
- 各种计算技术为开发预测模型提供了潜力.
研究的目的:
- 开发和比较用于预测二氧化碳吸收到甲胺溶剂的模型.
- 评估多层感知器 (MLP),辐射基函数 (RBF),支向量机 (SVM) 和响应表面方法 (RSM) 的性能.
- 确定最佳的网络架构和训练算法,以准确预测二氧化碳质量流量.
主要方法:
- 使用MLP,RBF,SVM和RSM来建模二氧化碳吸收.
- 输入参数包括溶剂密度,质量分数,温度,平衡常数,二氧化碳负载和部分压力.
- 使用trainlm,trainbr和trainscg算法训练的神经网络.
主要成果:
- 为一,二和三层确定了优化的MLP网络结构.
- 发现RBF的最佳差为2.202.2.
- 确定系数 (R2) 达到0.9996的MLP和0.9940的RBF,表明高精度.
- 训练lm功能表现出卓越的性能.
结论:
- 用特定算法训练的MLP和RBF网络有效模拟二氧化碳吸收.
- 这些计算模型能够准确地预测二氧化碳质量流量.
- 这些发现支持在化学过程设计和优化中使用先进的机器学习技术.
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