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 |
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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 |
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Country/Territory | Austria |
City | Innsbruck |
Period | 11/02/13 → 13/02/13 |
Keywords
- Gap analysis
- Genetic algorithms
- Global optimization
- Hydroprocessing
- Hydrotreating