Application of Deep Neural Network (dnn) for Experimental Liquid-Liquid Equilibrium Data of Water + Butyric Acid + 5-Methyl Ternary Systems

Abstract

LLE data are important for simulation and design of extraction equipment. In this study, deep neural network (DNN) structure was proposed for modelling of the ternary liquid-liquid equilibrium (LLE). LLE data of (water + butyric acid + 5-methyl-2-hexanone) ternaries defined at three different temperatures of 298.2, 308.2, and 318.2 K and P = 101.3 kPa, were obtained experimentally and then correlated with nonrandom two-liquid (NRTL) and universal quasi-chemical (UNIQUAC) models. The performance of the proposed DNN model was compared with that of NRTL and UNIQUAC in terms of the root mean square errors (RMSE). RMSE values were obtained between 0.02-0.06 for NRTL and UNIQUAC, respectively. For DNN, the error values were obtained between 0.00005-0.01 for all temperatures. According to the calculated RMSE values, it was shown that proposed DNN structure can be better choice for the modelling of LLE system. Othmer-Tobias and Hand correlations were also used for the experimental tie-lines. Distribution coefficient and separation factors were calculated from the experimental data.