Correlation of ternary liquid--liquid equilibrium data using neural network-based activity coefficient model

Abstract

Liquid--liquid equilibrium (LLE) data are important in chemical industry for the design of separation equipments, and it is troublesome to determine experimentally. In this paper, a new method for correlation of ternary LLE data is presented. The method is implemented by using a combined structure that uses genetic algorithm (GA)--trained neural network (NN). NN coefficients that satisfy the criterion of equilibrium were obtained by using GA. At the training phase, experimental concentration data and corresponding activity coefficients were used as input and output, respectively. At the test phase, trained NN was used to correlate the whole experimental data by giving only one initial value. Calculated results were compared with the experimental data, and very low root-mean-square deviation error values are obtained between experimental and calculated data. By using this model tie-line and solubility curve data of LLE can be obtained with only a few experimental data.