Development of a SVM model for Prediction of Hydrocracking Product Yields

Abstract

In this study Support Vector Machine (SVM) and Lump Kinetic Model was used to predict the hydrocracking products yield by using data which obtained from Tehran Refinery in Iran. The minimum calculated squared correlation coefficient and key parameters of SVM were used to evaluate the SVM performance. Output variables of SVM model were optimized for hydrocracking products. Kinetic parameter and mean squared error values for the 4-Lumped Kinetic models have been developed. Mean Squared Errors (MSE) of training set for Diesel, Naphtha and LPG were obtained 0.179, 0.116, and 0.174 and for testing set are 0.164, 0.148, and 0.132, respectively. It can be concluded that SVM can be used as a reliable and accurate estimation method with comparison between SVM data and Tehran refining unit. Such validated models may be regarded as valuable tools for process optimization, control, design, catalyst selection and provide a better insight into the process.