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The approach described in one of our previous papers (Jr. Taiwan Inst. Chem. Eng., 41, pp. 570Á578, 2010) was applied to the SoaveÁRedlichÁKwong equation of state to develop compound-specific parameters for four different types of cohesion factor models. Compoundspecific parameters for nearly 300 compounds are listed in the paper and can be utilized for the modelling of phase equilibrium of the mixtures of these compounds. Performance of the models was compared based on the accuracy in predicting various pure compound properties as well as vapour liquid equilibrium of binary systems. The modified Trebble Bishnoi (mGAS) type of cohesion factor model emerged as the best amongst the compared models. Generalization of compound-specific models was also done. These expressions would be useful in the absence of compound-specific parameters.

Molecular weight distribution, which is characterized by its averages like number average (Mn) and weight average (Mw), is one of the important properties of polybutadiene rubber (PBR), and it is difficult to measure. The objective of this work is to develop models to predict Mn and Mw from readily available process variables. Neural networks that are capable of mapping highly complex and non-linear dependencies have been adapted to develop models for the Mn and Mw of PBR. The molecular weight distribution and its averages of PBR samples collected over a wide range of operating conditions were measured by the conventional Gel Permeable Chromatograph (GPC) method. Neural networks were trained with relevant data to predict Mn and Mw from process variables. The trained networks were found to generalize well when tested with new data.