Gap analysis and optimization of hydroprocessing prediction model

Nedyalko Petrov; Ivan Jordanov

Gap analysis and optimization of hydroprocessing prediction model

Nedyalko Petrov^*
, Ivan Jordanov

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper discusses the process of gap identification, analysis and optimization of an existing hydrotreating process prediction model used in petroleum refineries. The performance of the model is investigated for a set of 16 specially selected crude oil feeds. Global optimization with genetic algorithm is conducted for a number of the model's parameters. The simulation, testing and validation of the investigated model show improved prediction accuracy and efficiency. MATLAB® is used as a main working environment for this investigation. Most of the tasks are automated and included in a graphical user interface tool that can assist the company for further model analysis, optimization, testing and validation of currently used models.

Original language	English
Title of host publication	IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
Publisher	ACTA Press
Pages	130-136
Number of pages	7
ISBN (Print)	9780889869431
Publication status	Published - 2013
Event	12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013 - Innsbruck, Austria Duration: 11 Feb 2013 → 13 Feb 2013

Conference

Conference	12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
Country/Territory	Austria
City	Innsbruck
Period	11/02/13 → 13/02/13

Keywords

Gap analysis
Genetic algorithms
Global optimization
Hydroprocessing
Hydrotreating

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https://www.iasted.org/conferences/pastinfo-793.html

Cite this

@inproceedings{2ef5a66ecc90404fa8203c478780b703,

title = "Gap analysis and optimization of hydroprocessing prediction model",

abstract = "This paper discusses the process of gap identification, analysis and optimization of an existing hydrotreating process prediction model used in petroleum refineries. The performance of the model is investigated for a set of 16 specially selected crude oil feeds. Global optimization with genetic algorithm is conducted for a number of the model's parameters. The simulation, testing and validation of the investigated model show improved prediction accuracy and efficiency. MATLAB{\textregistered} is used as a main working environment for this investigation. Most of the tasks are automated and included in a graphical user interface tool that can assist the company for further model analysis, optimization, testing and validation of currently used models.",

keywords = "Gap analysis, Genetic algorithms, Global optimization, Hydroprocessing, Hydrotreating",

author = "Nedyalko Petrov and Ivan Jordanov",

note = "publisher website and DOI not loading - 10.2316/P.2013.793-025; 12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013 ; Conference date: 11-02-2013 Through 13-02-2013",

year = "2013",

language = "English",

isbn = "9780889869431",

pages = "130--136",

booktitle = "IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013",

publisher = "ACTA Press",

address = "Canada",

}

Petrov, N & Jordanov, I 2013, Gap analysis and optimization of hydroprocessing prediction model. in IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013. ACTA Press, pp. 130-136, 12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013, Innsbruck, Austria, 11/02/13. <https://www.iasted.org/conferences/pastinfo-793.html>

TY - GEN

T1 - Gap analysis and optimization of hydroprocessing prediction model

AU - Petrov, Nedyalko

AU - Jordanov, Ivan

N1 - publisher website and DOI not loading - 10.2316/P.2013.793-025

PY - 2013

Y1 - 2013

N2 - This paper discusses the process of gap identification, analysis and optimization of an existing hydrotreating process prediction model used in petroleum refineries. The performance of the model is investigated for a set of 16 specially selected crude oil feeds. Global optimization with genetic algorithm is conducted for a number of the model's parameters. The simulation, testing and validation of the investigated model show improved prediction accuracy and efficiency. MATLAB® is used as a main working environment for this investigation. Most of the tasks are automated and included in a graphical user interface tool that can assist the company for further model analysis, optimization, testing and validation of currently used models.

AB - This paper discusses the process of gap identification, analysis and optimization of an existing hydrotreating process prediction model used in petroleum refineries. The performance of the model is investigated for a set of 16 specially selected crude oil feeds. Global optimization with genetic algorithm is conducted for a number of the model's parameters. The simulation, testing and validation of the investigated model show improved prediction accuracy and efficiency. MATLAB® is used as a main working environment for this investigation. Most of the tasks are automated and included in a graphical user interface tool that can assist the company for further model analysis, optimization, testing and validation of currently used models.

KW - Gap analysis

KW - Genetic algorithms

KW - Global optimization

KW - Hydroprocessing

KW - Hydrotreating

UR - https://www.scopus.com/pages/publications/84875540895

M3 - Conference contribution

AN - SCOPUS:84875540895

SN - 9780889869431

SP - 130

EP - 136

BT - IASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013

PB - ACTA Press

T2 - 12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013

Y2 - 11 February 2013 through 13 February 2013

ER -

Gap analysis and optimization of hydroprocessing prediction model

Abstract

Conference

Keywords

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