Online optimization of petrochemical process via case-based reasoning and conditional mutual information

The strict mechanism model of petrochemical processes is often complex, resulting in long modeling times, low computational efficiency, and poor accuracy, which limits the application of mechanistic models in process optimization and advanced control. The advent of big data technology provides new solutions for this problem. Herein, a new method based on historical Case-Based Reasoning (CBR) is proposed to process online optimization and calculate variable attribute weights for case retrieval using conditional mutual information. Considering the process time-lag characteristics, a piecewise sequential CBR method for optimization is further proposed, which directly recognizes the pattern based on the industrial past data and amply utilized the process information. For case study and method validation, the approach is applied to an actual fluid catalytic cracking (FCC) process, converting low-quality heavy oils into valuable light transportation fuels and chemicals such as gasoline and propylene. As demonstrated by the case study results, the proposed method increases the average yield of gasoline and total liquid respectively by 2.5 % and 4.0 % while decreasing the average yield of coke by 1.3 %. The results further indicate that the overall optimization performance is comparable to many advanced intellectual optimization algorithms, allowing favorable operability and ensuring good robustness for different optimization targets. It could provide a solution with high accuracy and good adaptability for online process optimization in complex chemical processes.