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Sensitivity analysis and optimization of the whole process of continuous catalytic reforming for Persian gulf star oil company using an optimized data-driven model with tuned parameters

  • Mahmud Atarianshandiz ORCID logo EMAIL logo and Akbar Shahsavand
Published/Copyright: February 5, 2025
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 20 Issue 1

Abstract

This paper applies an existing advanced model to improve key outputs in the continuous catalytic reforming (CCR) process for Persian Gulf Star Oil Company. Using tools like Aspen Custom Modeler and Aspen Plus, we focus on optimizing two main results: Research Octane Number (RON) and yield. A design of experiments was conducted to examine the effects of key input variables, including reactor temperatures and the hydrogen-to-hydrocarbon (H₂/HC) ratio, through 256 simulations. Various data fitting methods, including Response Surface Methodology (RSM), Radial Basis Function Network (RNLOOCV), and Artificial Neural Networks (ANN), were applied to describe process behavior. The Akaike Information Criterion (AIC)-optimized ANN model demonstrated the best performance, offering a balanced approach between accuracy and complexity. A sensitivity analysis revealed that increasing reactor temperatures improves RON but reduces yield due to enhanced cracking reactions. The H₂/HC ratio had a minimal impact on RON and yield, primarily serving to limit catalyst coke formation. Optimization using a genetic algorithm confirmed that optimal RON and yield can be achieved within specific temperature ranges. The results provide insights for enhancing CCR efficiency and refinery profitability.

Keywords: mathematical model; naphtha reforming; continuous catalytic regeneration; genetic algorithm; sensitivity analysis
Corresponding author: Mahmud Atarianshandiz, Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: All data generated or analyzed during this study are included in this published article.

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Received: 2024-11-13
Accepted: 2024-12-28
Published Online: 2025-02-05

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cppm-2024-0112
Keywords for this article
mathematical model; naphtha reforming; continuous catalytic regeneration; genetic algorithm; sensitivity analysis

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Cogeneration system’s energy performance improvement by using P-graph and advanced process control
  4. Numerical simulation of R134a evaporation in a cold water production system
  5. Implementing a radial basis function model to anticipate the outcomes of the gasification
  6. Sensitivity analysis and optimization of the whole process of continuous catalytic reforming for Persian gulf star oil company using an optimized data-driven model with tuned parameters
  7. Evaluating the therapeutic potential of 4-hydroxyflavanes diastereomers derivatives against (MetAP2) for anti-cancer therapy: a molecular docking study
  8. Enhanced cryogenic distillation column identification for methane separation: a hybrid artificial neural network approach
  9. Natural Gas and hydrogen blending: a perspective on numerical modeling and CFD analysis for transient and steady-state scenarios
  10. Simulation and optimization of Venturi type bubble generator to improve cavitation
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