ANN and RSM Modelling and Optimization of Paraffins and Aromatics in Crude Oil Distillation Products’ Properties in Iraq

Abstract

Back-Propagation neural networks, as well as RSM-DOE techniques, were used to predict the properties of various compositions of Iraqi oil, were presented in this study. Paraffin’s and Aromatics’ effect on petroleum properties, e.g., yield, density, calorific value, and other essential properties, were studied. The input-output data to the neural networks were obtained from existing local refineries in Iraq. Several network architectures were tried, and the networks that best simulate the hydrocracking process were retained. The predictions of the prepared neural networks have been cross-validated against data not initially used in the training process. The networks compared well against this new set of data, with an average per cent error always less than 5 for the various products of the hydrocracking unit.