Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm

In this study, genetic algorithm was used to predict the reaction kinetics of Iraqi heavy naphtha catalytic reforming process located in Al-Doura refinery in Baghdad.  One-dimensional steady state model was derived to describe commercial catalytic reforming unit consisting of four catalytic reformi...

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Main Authors: Ramzy H. Saihod, Zaidoon M. Shakoor, Abbas A. Jawad
Format: Article
Language:English
Published: Al-Khwarizmi College of Engineering – University of Baghdad 2017-12-01
Series:Al-Khawarizmi Engineering Journal
Subjects:
Online Access:http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/190
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spelling doaj-c4818898be8b45a39ce8ac9d43b10c252020-11-24T23:54:51Zeng Al-Khwarizmi College of Engineering – University of BaghdadAl-Khawarizmi Engineering Journal1818-11712312-07892017-12-01101Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic AlgorithmRamzy H. Saihod0Zaidoon M. Shakoor1Abbas A. Jawad2Department of Petroleum Technology / University of TechnologyDepartment of Chemical Engineering / University of TechnologyAl-Doura Refinery / Middle Refinery Company In this study, genetic algorithm was used to predict the reaction kinetics of Iraqi heavy naphtha catalytic reforming process located in Al-Doura refinery in Baghdad.  One-dimensional steady state model was derived to describe commercial catalytic reforming unit consisting of four catalytic reforming reactors in series process. The experimental information (Reformate composition and output temperature) for each four reactors collected at different operating conditions was used to predict the parameters of the proposed kinetic model. The kinetic model involving 24 components, 1 to 11 carbon atoms for paraffins and 6 to 11 carbon atom for naphthenes and aromatics with 71 reactions. The pre-exponential Arrhenius constants and activation energies were determined after fine tuning of the model results with experimental data. The input to the optimization is the compositions for 21 components and the temperature for the effluent stream for each one of the four reactors within the reforming process while the output of optimization is 142 predicted kinetic parameters for 71 reactions within reforming process.  The differential optimization technique using genetic algorithm to predict the parameters of the kinetic model. To validate the kinetic model, the simulation results of the model based on proposed kinetic model was compared with the experimental results. The comparison between the predicted and commercially results shows a good agreement, while the percentage of absolute error for aromatics compositions are (7.5, 2, 8.3, and 6.1%) and the temperature absolute percentage error are (0.49, 0.5, 0.01, and 0.3%) for four reactors respectively.    http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/190Heavy NaphthaReformingGenetic AlgorithmOptimizationReaction Kinetic
collection DOAJ
language English
format Article
sources DOAJ
author Ramzy H. Saihod
Zaidoon M. Shakoor
Abbas A. Jawad
spellingShingle Ramzy H. Saihod
Zaidoon M. Shakoor
Abbas A. Jawad
Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
Al-Khawarizmi Engineering Journal
Heavy Naphtha
Reforming
Genetic Algorithm
Optimization
Reaction Kinetic
author_facet Ramzy H. Saihod
Zaidoon M. Shakoor
Abbas A. Jawad
author_sort Ramzy H. Saihod
title Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
title_short Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
title_full Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
title_fullStr Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
title_full_unstemmed Prediction of Reaction Kinetic of Al- Doura Heavy Naphtha Reforming Process Using Genetic Algorithm
title_sort prediction of reaction kinetic of al- doura heavy naphtha reforming process using genetic algorithm
publisher Al-Khwarizmi College of Engineering – University of Baghdad
series Al-Khawarizmi Engineering Journal
issn 1818-1171
2312-0789
publishDate 2017-12-01
description In this study, genetic algorithm was used to predict the reaction kinetics of Iraqi heavy naphtha catalytic reforming process located in Al-Doura refinery in Baghdad.  One-dimensional steady state model was derived to describe commercial catalytic reforming unit consisting of four catalytic reforming reactors in series process. The experimental information (Reformate composition and output temperature) for each four reactors collected at different operating conditions was used to predict the parameters of the proposed kinetic model. The kinetic model involving 24 components, 1 to 11 carbon atoms for paraffins and 6 to 11 carbon atom for naphthenes and aromatics with 71 reactions. The pre-exponential Arrhenius constants and activation energies were determined after fine tuning of the model results with experimental data. The input to the optimization is the compositions for 21 components and the temperature for the effluent stream for each one of the four reactors within the reforming process while the output of optimization is 142 predicted kinetic parameters for 71 reactions within reforming process.  The differential optimization technique using genetic algorithm to predict the parameters of the kinetic model. To validate the kinetic model, the simulation results of the model based on proposed kinetic model was compared with the experimental results. The comparison between the predicted and commercially results shows a good agreement, while the percentage of absolute error for aromatics compositions are (7.5, 2, 8.3, and 6.1%) and the temperature absolute percentage error are (0.49, 0.5, 0.01, and 0.3%) for four reactors respectively.   
topic Heavy Naphtha
Reforming
Genetic Algorithm
Optimization
Reaction Kinetic
url http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/190
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AT zaidoonmshakoor predictionofreactionkineticofaldouraheavynaphthareformingprocessusinggeneticalgorithm
AT abbasajawad predictionofreactionkineticofaldouraheavynaphthareformingprocessusinggeneticalgorithm
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