Gap analysis and optimization of hydroprocessing prediction model

Nedyalko Petrov*, Ivan Jordanov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationIASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
PublisherACTA Press
Pages130-136
Number of pages7
ISBN (Print)9780889869431
Publication statusPublished - 2013
Event12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013 - Innsbruck, Austria
Duration: 11 Feb 201313 Feb 2013

Conference

Conference12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
Country/TerritoryAustria
CityInnsbruck
Period11/02/1313/02/13

Keywords

  • Gap analysis
  • Genetic algorithms
  • Global optimization
  • Hydroprocessing
  • Hydrotreating

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