Prediction of acid gases solubilities in ionic liquids (ILs), have recently emerged as promising mediumsfor refining of natural gas, using powerful paradigms is of great importance from technical and eco-nomical point of view. In this respect, this study aims at appraising the effectiveness of one of the newgeneration soft computing methodologies called gene-expression programming (GEP) for estimating thehydrogen sulfide (H2S) solubility in ionic liquids (ILs). A total data set of 465 experimental data belong-ing to 11 ionic liquids, which gathered from literatures, were used to develop a general correlation.The temperature and pressure accompanied with acentric factors and critical temperature and pressureof ILs were used as independent input variables, while H2S solubility as dependent output variables.The modeling results showed the coefficient of determination (R2) of 0.9902 and 0.0438% mean abso-lute relative error (MARE) for the predicted solubilities from the corresponding experimental values.Therefore, the model is comprehensive and accurate enough to be used to predict the H2S solubility invarious ILs. In addition, the GEP-model predictions were compared with the outputs of two well-knownengineering approaches named Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR). Results showedthat the proposed evolutionary-based method was more accurate than the widely used aforementionedthermodynamic models.
کلید واژگان :Ionic liquidsHydrogen sulfideSolubilityPredictionEquation of stateGenetic expression programming
ارزش ریالی : 1200000 ریال
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