DOI: https://doi.org/10.9767/bcrec.8.2.4722.125-136

Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor

1Research Institute of Petroleum Industry (RIPI), Catalysis and Nanotechnology Research Division, West Blvd., Azadi Sport complex, P.O. Box 14665137, Tehran, Iran, Islamic Republic of

2Chemical & Biochemical Engineering Department, Missouri University of Science & Technology, Rolla, United States

Received: 9 Apr 2013; Revised: 13 Aug 2013; Accepted: 17 Aug 2013; Available online: 7 Dec 2013; Published: 30 Dec 2013.
Editor(s): Istadi Istadi
Open Access Copyright (c) 2013 by Authors, Published by BCREC Group
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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Abstract
An artificial neural network (ANN) and kinetic-based models for a pilot scale vacuum gas oil (VGO) hydrocracking plant are presented in this paper. Reported experimental data in the literature were used to develop, train, and check these models. The proposed models are capable of predicting the yield of all main hydrocracking products including dry gas, light naphtha, heavy naphtha, kerosene, diesel, and unconverted VGO (residue). Results showed that kinetic-based and artificial neural models have specific capabilities to predict yield of hydrocracking products. The former is able to accurately predict the yield of lighter products, i.e. light naphtha, heavy naphtha and kerosene. However, ANN model is capable of predicting yields of diesel and residue with higher precision. The comparison shows that the ANN model is superior to the kinetic-base models. © 2013 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)
Keywords: Modeling; Artificial Neural Network; Kinetic; Hydrocracking

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Section: Original Research Articles
Language : EN
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