Abstract
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.