Kh Sharifi - Department of Chemical Engineering, Iran University of Science and Technology, Narmak Street, Tehran, Iran
M Shirvani - Department of Chemical Engineering, Iran University of Science and Technology, Narmak Street, Tehran, Iran
M Bahmani - Department of Chemistry, Applied Chemistry Group, Tarbiat Moalem University, Dr. Mofatteh Street, Tehran, Iran

In this contribution Artificial Neural Network (ANN) modeling of the hydrocracking process is presented. The input–output data for the training and simulation phases of the network were obtained from the Tehran refinery ISOMAX unit. Backpropagation networks of different architectures were developed and the networks that best simulated the plant data were retained. The trained networks predicted the yields of products of the ISOMAX unit (diesel, kerosene, light naphtha and heavy naphtha) with only 4% error. The residual error (root mean squared difference) between the model predictions and plant data indicated that the validated model could be reliably used to simulate the ISOMAX unit. Such validated models are valuable tools for refineries for process optimization, control, design, catalyst selection and a better understanding of the process operation.

Artificial neural networks, Hydrocracking process, Product yields