Comparison of kinetic-based and artificial neural network modeling methods for a pilot scale vacuum gas oil hydrocracking reactor

被引：0
作者：
机构：
[1] Research Institute of Petroleum Industry (RIPI), Catalysis and Nanotechnology Research Division, Azadi Sport Complex, Tehran, West Blvd.
[2] Chemical and Biochemical Engineering Department, Missouri University of Science and Technology, Rolla
来源：
Sadighi, S. (sadighis@ripi.ir) | 1600年 / Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)卷 / 08期
关键词：
Artificial neural network; Hydrocracking; Kinetic; Modeling;
D O I：
10.9767/bcrec.8.2.4722.125-136
中图分类号：
学科分类号：
摘要：
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 BCREC UNDIP. All rights reserved.
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页码：125 / 136
页数：11
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