Generalized NRTL model for predicting vapor-liquid equilibrium data from activity coefficient of binary component systems: using molecular descriptors
Abstract
Vapor-Liquid Equilibrium data is crucial for separation processes like distillation, extraction and manufacturing. Obtaining this data experimentally for desired conditions and systems is time-consuming and expensive. Therefore there is a necessity for an a priori generalized model which predicts this data based on the molecular descriptor information (of the desired system) given as an input to the model. This model is based on the Non-Random Two-Liquid (NRTL) model to predict binary interaction (NRTL) parameters. These predicted parameters are, in turn, used to calculate the activity coefficient, which is used to calculate the vapor-phase composition from the liquid-phase composition of the system. In this study, the molecular descriptors for individual components of the 28 binary systems were generated. The arithmetic mean of the molecular descriptors of the corresponding components was used as the molecular descriptor set for that binary system. The molecular descriptors based on properties relevant to vapor-liquid equilibrium were selected and used as independent variables to build the model using an Artificial Neural Network (ANN) in python. Better predictions were obtained with the coefficient of determination greater than 0.85 for each NRTL parameter. Once the liquid-phase composition is known, the model can predict the vapor-phase composition at the desired pressure and temperature.
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