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Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit

This paper presents an artificial neural network (ANN) model of heavy oil catalytic cracking (HOC). The main feature of the model isto provide general and accurate and fast responding model for analysis of HOC unit. In this study, American petroleum institute index(API) , weight percentage of sulfur...

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Main Authors: Sheikhattar, Leila, Hashim, Haslenda, Zahedi, Gholamreza
Format: Article
Published: World Academy of Research and Publication 2011
Subjects:
QD Chemistry
Online Access:http://eprints.utm.my/id/eprint/6963/
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spelling my.utm.69632017-02-15T00:30:40Z http://eprints.utm.my/id/eprint/6963/ Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit Sheikhattar, Leila Hashim, Haslenda Zahedi, Gholamreza QD Chemistry This paper presents an artificial neural network (ANN) model of heavy oil catalytic cracking (HOC). The main feature of the model isto provide general and accurate and fast responding model for analysis of HOC unit. In this study, American petroleum institute index(API) , weight percentage of sulfur, Conradson carbon residue content (CCR), gas, coke, and liquid volume percent conversion (%LV)of reaction were considered as network inputs while the percentage of normal butane (N-C4), iso-butane (I-C4), butene (C4=), propane(C3), propene (C3=), heavy cycle oil (HCO), and light cycle oil (LCO) and gasoline (GASO) were considered as network outputs. 70%of all industrial collected data set were utilized to train and find the best neural network. Among the different networks, feed-forwardmulti-layer perceptron network with Levenberg Marquardt (LM) training algorithm with 10 neurons in hidden layer was found as thebest network. The trained network showed good capability in anticipating the results of the unseen data (30% of the all data) of catalytic cracking unit with high accuracy. The obtained model can be used in optimization and process planning. World Academy of Research and Publication 2011 Article PeerReviewed Sheikhattar, Leila and Hashim, Haslenda and Zahedi, Gholamreza (2011) Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit. International Journal of Chemical and Environmental Engineering, 2 (1). pp. 7-12. ISSN 20780737 (Unpublished)
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QD Chemistry
spellingShingle QD Chemistry
Sheikhattar, Leila
Hashim, Haslenda
Zahedi, Gholamreza
Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit
description This paper presents an artificial neural network (ANN) model of heavy oil catalytic cracking (HOC). The main feature of the model isto provide general and accurate and fast responding model for analysis of HOC unit. In this study, American petroleum institute index(API) , weight percentage of sulfur, Conradson carbon residue content (CCR), gas, coke, and liquid volume percent conversion (%LV)of reaction were considered as network inputs while the percentage of normal butane (N-C4), iso-butane (I-C4), butene (C4=), propane(C3), propene (C3=), heavy cycle oil (HCO), and light cycle oil (LCO) and gasoline (GASO) were considered as network outputs. 70%of all industrial collected data set were utilized to train and find the best neural network. Among the different networks, feed-forwardmulti-layer perceptron network with Levenberg Marquardt (LM) training algorithm with 10 neurons in hidden layer was found as thebest network. The trained network showed good capability in anticipating the results of the unseen data (30% of the all data) of catalytic cracking unit with high accuracy. The obtained model can be used in optimization and process planning.
format Article
author Sheikhattar, Leila
Hashim, Haslenda
Zahedi, Gholamreza
author_facet Sheikhattar, Leila
Hashim, Haslenda
Zahedi, Gholamreza
author_sort Sheikhattar, Leila
title Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit
title_short Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit
title_full Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit
title_fullStr Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit
title_full_unstemmed Artificial neural network simulation and sensitivity analysis of heavy oil cracking unit
title_sort artificial neural network simulation and sensitivity analysis of heavy oil cracking unit
publisher World Academy of Research and Publication
publishDate 2011
url http://eprints.utm.my/id/eprint/6963/
_version_ 1643644676371120128
score 13.211869

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