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

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doi:10.1002/ceat.70137

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

2025-12-09

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DOI：10.1002/ceat.70137

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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.

Keywords:

Bayesian statistics

case-based reasoning

data-driven optimization

fluid catalytic cracking

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

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