Modelling the Solubility of 1,1,1,2-Tetrafluoroethane, 1-Chloro-1,1-difluoroethane, Butane and Iso-butane in LDPE with Artificial Neural Network

Abstract

T he solubility of free monomers in polymer has an effective role in the product quality and on environment in various steps of polymer manufacturing. Therefore, it is important to find confident method to predict the solubility of monomers in polymers. For predicting the solubility of gaseous monomers in polymers, among the various methods used, equations of state are considered as the most effective tools due to the simplicity in their applications. However, the accurate solubility prediction with equations of state needs suitable binary interaction parameters. Unfortunately, these parameters often have unknown temperature and solute composition functional- ity and therefore, other empirical correlations methods are used. The correlation meth- ods, however, generally consist of equations having several parameters which are to be evaluated from experimental data for each system at a given temperature and this imposes limitation on their applications. Artificial neural network (ANN) can be a pow- erful alternative tool for predicting gaseous monomers in polymers. In this work, the sol- ubility of 1,1,1,2-tetrafluoroethane (HFC-134a), 1-chloro-1,1-difluoroethane (HCFC- 142b), butane and iso-butane in low-density polyethylene (LDPE) has been studied by ANN using back propagation method (BP). It was found that a 2-4-1 architecture can predict the gas solubility satisfactorily.