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Gholam Reza Zahedi
Chemical & Biochemical Engineering Department, Missouri University of Science & Technology, Rolla
Chemical Engineering, Chemistry & Bioengineering Chemistry

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Journal : Bulletin of Chemical Reaction Engineering

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Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor Sepehr Sadighi; Gholam Reza Zahedi
Bulletin of Chemical Reaction Engineering & Catalysis 2013: BCREC Volume 8 Issue 2 Year 2013 (December 2013)
Publisher : Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/bcrec.8.2.4722.125-136

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)
Co-Authors Sepehr Sadighi