Data‐Driven Online Optimization for Fluid Catalytic Cracking Using Bayesian Case‐Based Reasoning

ABSTRACT Traditional data‐driven optimization methods using case‐based reasoning (CBR) rely on heuristic similarity matching and lack probabilistic rigor, especially in complex processes like fluid catalytic cracking (FCC) with high dimensionality and uncertainty. To address these challenges, a novel data‐driven framework that integrates compact posterior estimation with CBR is proposed. The method first identifies key variables affecting product yields through information‐theoretic dimensionality reduction. Optimal operating parameters are then inferred using a combination of K‐nearest neighbors for similarity matching and Markov Chain Monte Carlo sampling for probabilistic estimation. Industrial validation showed gasoline and total liquid yields increased by 7.31% and 6.94%, respectively, with coke yield reduced by 5.83%. This approach successfully improves computational efficiency and optimization accuracy in practical applications.