LAUSR

Abstract In this study, we introduced an artificial neural network (ANN), which can represent objects that are difficult to formulate, rather than the integrated model of the group contribution method (GCM), because the integrated model in the existing GCM is no longer able to manage enormous diversification of substances. Hence, a model to estimate the pure component parameters m, σ, and e of the perturbed chain statistical associating fluid theory (PC-SAFT) equation of state (EoS) was developed. We optimized the structure of the ANN by changing the number of neurons and layers in the hidden layer. In this study, the optimized ANN structure was two hidden layers and 40 neurons. The results confirm that the model can determine the pure component parameters of PC-SAFT EoS, which can estimate the liquid density, saturated vapor pressure, and critical properties. In terms of the critical properties, the estimated results were almost as good or better than those obtained using the GCMs, which are specified for the critical properties. We were able to calculate properties for substances whose parameters were not reported in the literature by using this ANN. The results would be useful for chemical process design, for example, to be incorporated into process simulators.