Multi-Objective Sustainable Operational Optimization of Fluid Catalytic Cracking

Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-objective optimization challenge in FCC operations, seeking to simultaneously maximize the gasoline production and minimize the coke yield—the latter being directly linked to C O 2 emissions in FCC. A data-driven optimization model leveraging a dual Long Short-Term Memory architecture is developed to capture complex relationships between operating variables and product yields. To efficiently solve the model, an Improved Multi-Objective Whale Optimization Algorithm (IMOWOA) is proposed, integrating problem-specific adaptive multi-neighborhood search and dynamic restart mechanisms. Extensive experimental evaluations demonstrate that IMOWOA achieves superior convergence characteristics and comprehensive performance compared to established multi-objective algorithms. Relative to the yields before optimization, the proposed methodology increases the gasoline yield by 0.32% on average, coupled with an average reduction of 0.11% in the coke yield. For the studied FCC unit with an annual processing capacity of 2.6 million tons, the coke reduction corresponds to an annual C O 2 emission reduction of approximately 10,277 tons, delivering benefits to sustainable FCC operations.