Phase equilibria modeling of binary systems containing ethanol using optimal feedforward neural network

•Optimal ANN is applied for VLE estimation of nine binary systems containing C2H5OH.•Our ANN model predict vapor mole fraction with %AARD of 1.525 and MSE 1.27 × 10−05.•Proposed ANN model estimate bubble point pressure with %AARD of 2.59 and MSE 0.00019.•Proposed method has been benchmarked with EOS proposed by other researchers.•Results confirm that our ANN model is more accurate than another available works.

A comprehensive understanding of vapor liquid equilibrium (VLE) data is one of the most important information for designing and modeling of process equipment. Because, it is not always possible to completely carry out experiments at all of the possible operational temperatures and pressures range, generalized thermodynamic models, e.g. equations of state are constructed for computing of required VLE data. In this work, artificial neural network (ANN) was used to derive predictive models of bubble point pressure and vapor phase composition of binary ethanol (C2H5OH) mixtures. In the neural network model, it is assumed that the considered VLE data depend on critical properties, acentric factor, normal boiling point, liquid phase composition of the solutes, and temperature. The proposed ANN model has been constructed and trained with VLE experimental data of nine different binary systems containing C2H5OH collected from various literatures. Optimal configuration of the ANN model has been determined using minimizing the average absolute relative deviation percent (%AARD), mean square errors (MSE) and the maximizing the correlation coefficient (R2) between observed and predicted VLE data with the ANN model. By using this procedure a two-layer ANN model with twenty-three hidden neuron has been found as an optimal topology. The accuracy of our optimal two layers ANN model has been compared with the Peng–Robinson cubic equation combined with Wong–Sandler (WS) mixing rules including a Van Laar (VL) model for the excess Gibbs free energy. Comparison with available literatures data and Peng–Robinson equation of state confirm that the present ANN model is more accurate and superior than the other published works. The sensitivity errors analysis clarify that our ANN model could predict vapor phase composition and bubble point pressure of all of the nine binary ethanol systems with %AARD of 1.52% and 2.59% respectively. The study demonstrates that the neural network model is a good alternative method for the estimation of VLE properties of the binary system containing C2H5OH.

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