Characterization of basic properties for pure substances and petroleum fractions by neural network

A set of conventional feedforward multilayer neural networks have been proposed to predict basic properties (e.g., critical temperature ( T c), critical pressure ( P c), critical volume ( V c), acentric factor ( ω) and molecular weight (MW)) of pure compounds and petroleum fractions based on their normal boiling point ( T b) and liquid density at 293 K. The accuracy of the method is evaluated by its application for basic property estimation of various components not used in the development of the method. Furthermore, the performance of the method is compared against the performance of the other alternatives reported as the most accurate and general methods for basic property prediction. Results of this comparison show that the proposed method outperforms the other alternatives using the same parameters, both in accuracy and generality. Furthermore, the accuracy of the proposed method is almost similar to the accuracy of the only general method despite the fact that the number of parameters required for the proposed method is less than the other alternative.