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Isobaric vapour-liquid equilibrium calculations of binary systems using neural network

Bilgin Mehmet (Istanbul University, Engineering Faculty, Department of Chemical Engineering, Istanbul, Turkey)

A model on a feed forward back propagation neural network was employed to calculate the isobaric vapour–liquid equilibrium (VLE) data at 40, 66.67, and 101.32 ±0.02 kPa for the methylcyclohexane – toluene and isopropanol – methyl isobutyl ketone binary systems, which are composed of different chemical structures (cyclic, aromatic, alcohol and ketone) and do not show azeotrope behaviour. Half of the experimental VLE data only were assigned into the designed framework as training patterns in order to estimate the VLE data over the whole composition range at the mentioned pressures. The results were compared with the data calculated by the two classical models used in this field, the UNIFAC and Margules models. In all cases the deviations the experimental activity coefficients and those calculated by the neural network model (NNET) were lower than those obtained using the Margules and UNIFAC models.

Keywords: vapour–liquid, neural network, activity coefficients, UNIFAC, Margules model