Modeling of supercritical ethane extraction by artificial neural networks

In this paper, an artificial neural network that considers the system as a black box is designed for the mass transfer modeling of supercritical ethane extraction. In addition, a hybrid model using both neural network and Peng-Robinson state equation is developed for supercritical ethane extraction, where the neural network is used to generate the nonlinear binary interaction parameter of the Peng-Robinson state equation. The predictions of the proposed neural network models are compared to a conventional model with a Peng-Robinson equation of state in literature. Generally, the results using the proposed models are better than those using the conventional model.