Gap Analysis and Optimization of Hydroprocessing Prediction Model

Nedyalko Petrov and Ivan Jordanov


gap analysis, global optimization, hydrotreating, hydroprocessing, genetic algorithms.


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.

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