Multiobjective optimization and analysis of petroleum refinery catalytic processes: A review

Hamdi A. Al-Jamimi, Galal M. BinMakhashen, Kalyanmoy Deb, Tawfik A. Saleh*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

29 Scopus citations


Multiobjective optimization (MOO) techniques are of much interest with their applications to petroleum refinery catalytic processes for finding optimal solutions in the midst of conflicting objectives. The rationale behind using MOO is that if objectives are in conflict, a set of trade-off optimal modeling solutions must be obtained to help management select the most-preferred operational solution for a refinery process. Using MOO does not involve hyperparameters thereby reducing the expensive parameter tuning tasks. A true MOO method allows numerous Pareto-based optimal solutions to be identified so that management and decision-makers' preference information can be used to finally select a single preferred solution. This review discusses MOO algorithms and their applications in petroleum and refinery processes. The survey provides insights into the fundamentals, metrics, and relevant algorithms conceived for MOO in petroleum and refinery fields. Also, it provides a deeper discussion of state-of-the-art research conducted to optimize conflicting objectives simultaneously for three main refinery processes, namely hydrotreating, desulfurization, and cracking. Finally, several research and application directions specific to refinery processes are discussed.

Original languageEnglish
Article number119678
StatePublished - 15 Mar 2021

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Ltd


  • Environmentally friendly approach
  • Evolutionary computing
  • Multiobjective optimization
  • Pareto-optimal solution
  • Petroleum refinery

ASJC Scopus subject areas

  • General Chemical Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Organic Chemistry


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