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Prediction of H 2 S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks
Author(s) -
BaratiHarooni Ali,
Nasery Saeid,
Tatar Afshin,
NajafiMarghmaleki Adel,
Isafiade Adeniyi Jide,
Bahadori Alireza
Publication year - 2017
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201600110
Subject(s) - ionic liquid , solubility , alkanolamine , artificial neural network , hydrogen sulfide , electrolyte , amine gas treating , chemistry , radial basis function , multilayer perceptron , thermodynamics , perceptron , biological system , organic chemistry , computer science , aqueous solution , artificial intelligence , physics , sulfur , electrode , catalysis , biology
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 2 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.