• International Journal of Technology (IJTech)
  • Vol 9, No 1 (2018)

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

Sepehr Sadighi, Seyed Reza Seif Mohaddecy, Ali Abbasi

Corresponding email: sadighis@ripi.ir

Published at : 27 Jan 2018
Volume : IJtech Vol 9, No 1 (2018)
DOI : https://doi.org/10.14716/ijtech.v9i1.44

Cite this article as:
Sadighi, S., Mohaddecy, S.R.S., Abbasi, A., 2018. Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network. International Journal of Technology. Volume 9(1), pp. 99-109

Sepehr Sadighi Research Institute of Petroleum Industry (RIPI), West side of Azadi Complex
Seyed Reza Seif Mohaddecy Research Institute of Petroleum Industry (RIPI), West side of Azadi Complex
Ali Abbasi Research Institute of Petroleum Industry (RIPI), West side of Azadi Complex
Email to Corresponding Author


In this research, based on actual data gathered from an industrial scale vacuum gas oil (VGO) hydrocracker and artificial neural network (ANN) method, a model is proposed to simulate yields of products including light gases, liquefied petroleum gas (LPG), light naphtha, heavy naphtha, kerosene, diesel and unconverted oil (off-test). The input layer of the ANN model consists of the catalyst, feed and recycle flow rates, and bed temperatures, while the output neurons are yields of those products. The results showed that the AAD% (average absolute deviation) of the developed ANN model for training, testing, and validating data are 0.445%, 1.131% and 0.755%, respectively. Then, by considering all operational constraints, the results confirmed that the decision variables (i.e., recycle rate and bed temperatures) generated by the optimization approach can enhance the gross profit of the hydrocracking process to more than $0.81 million annually, which is significant for the economy of the target refinery.

Artificial neural network; Hydrocracking; Modeling; Optimization; Vacuum gas oil


An industrial scale VGO hydrocracking unit was modeled using the ANN method. This model was designed based on a feed-forward neural network with seven neurons in the input layer and three neurons in the hidden layer. The input process variables were catalyst life, fresh and recycle feed flow rates, and the input temperature to each catalytic bed. For all VGO hydrocracking products (i.e., light gases, LPG, light and heavy naphtha, kerosene, diesel, and unconverted oil), the ANN model was trained, tested, and validated. The results showed that ANN could simulate yield of products for 69 operating points during 557 days with an AAD% of 1.2%.

To propose an application for the constructed ANN model, the gross profit function of the VGO hydrocracking plant was maximized by considering some limitations in the process. The results showed that within the permissible range for the manipulated variables (i.e., the recycle flow rate and bed temperatures), the average gross profit of the target unit could be increased to $810,000 per year. Furthermore, due to the reduction in the temperature of hydrocracking beds after optimization, it was possible to reduce the coke formation on the catalyst surface, which subsequently would increase the cycle life of the catalyst.


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