![Sample our Earth Sciences journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5930/TOC-Subject-Sample-2021-Earth-Sciences.jpg"})
Abstract
Vapor liquid equilibrium (VLE) data are important for designing and modeling of process equipments. Since it is not always possible to carry out experiments at all possible temperatures and pressures, generally thermodynamic models based on equations of state are used for estimation of VLE so that new models are then highly required. Therefore, an effort has been made to develop an alternative to a classical equation of state. The authors outline a new approach using a neural fuzzy model based on an adaptive network–based fuzzy inference system (ANFIS) was proposed to high-pressure VLE-related literature data to develop and validate a model capable of predicting VLE for the binary systems. The statistical methods, such as the mean absolute percentage error, mean square error, root mean squared errors, and the coefficient of multiple determinations (R 2) are given to compare the predicted and actual values for model validation. Furthermore, the comparison in terms of statistical values between the predicted results for the whole temperature range and literature results are predicted by the conventional Redlich-Kwang-Soave equation of state. The ANFIS model predictions showed better agreement with experimental data than the thermodynamic model predictions.