Prediction of H<sub>2sub>S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks

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• 作者：Ali Barati-Harooni ; Saeid Nasery ; Afshin Tatar ; Adel Najafi-Marghmaleki ; Adeniyi Jide Isafiade and Alireza Bahadori
• 刊名：Chemical Engineering & Technology
• 出版年：2017
• 出版时间：February, 2017
• 年：2017
• 卷：40
• 期：2
• 页码：367-375
• 全文大小：452K
• ISSN：1521-4125

文摘

Industrial natural gas treating plants commonly employ amine-based treatments for hydrogen sulfide elimination from crude oil and gas. Some deficiencies boost the motivation to find an appropriate alternative. Due to their advantageous properties, liquid electrolytes are considered as possible substitutes for classical alkanolamine solvents in such processes. The solubility of gases in ionic solutions at different temperatures and pressures is a crucial factor in the examination of ionic liquids as a potential alternative. Two intelligent methods, namely, simple multilayer perceptron (MLP) and radial basis function neural networks, are proposed to accurately predict the solubility of H<sub>2sub>S in various ionic liquids. The predicted values agree well with the experimental data. A comparison to other intelligent models, which were recently suggested, reveals the superiority of the proposed simple MLP model.