使用神经网络模型,探索CO2在1-N-基-3-甲基利米达六酸离子液中的可溶性
Hadiseh Masoumi1, Bahador Daryayehsalameh2, Ahad Ghaemi3
1Department of Applied Chemistry, Faculty of Chemistry and Petroleum Sciences, Bu-Ali Sina University, Hamedan, 6517838683, Iran. h.masoumi@basu.ac.ir.
这项研究开发了神经网络模型,以估计用于碳捕获应用的1-N-butanyl-3-methylimidazolium hexafluorophosphate离子液中的CO2分子分数. 多层感知子模型表现出更高的准确性,尽管其预测性能在高温下降.
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
背景情况:
- 碳捕获对于缓解气候变化至关重要.
- 离子液体,如[Bmim][PF6],对二氧化碳吸收有很大的希望.
- 在离子液体中实验CO2溶解性研究可能具有挑战性.
研究的目的:
- 开发和比较人工神经网络模型,用于预测[Bmim][PF6]中的CO2分子分数.
- 确定最准确的模型来估计在不同条件下的二氧化碳溶解度.
主要方法:
- 开发多层感知器 (MLP) 和辐射基函数 (RBF) 神经网络模型.
- 使用Levenberg-Marquardt算法与特定的激活函数来训练MLP模型.
- 确定MLP模型的权重矩阵,偏差向量和激活函数.
主要成果:
- 确定MLP神经网络模型是估计CO2分子分数最准确的.
- 在较低的温度 (283.15 K) 中,MLP模型实现了较低的相对偏差.
- 在较高温度 (298.15 K和323.15 K) 时,模型的预测准确性显著下降.
结论:
- MLP神经网络提供了一种可行的计算方法,用于模拟离子液体中的二氧化碳溶解度.
- 开发的MLP模型的准确性取决于温度,在更高的温度下显示限制.
- 进一步的研究可能将重点放在改善CO2捕获应用的更广泛温度范围的模型性能上.
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