An online optimization strategy for a fluid catalytic cracking process using a case-based reasoning method based on big data technology

Rigorous mechanistic models of refining processes are often too complex, which results in long modeling times, low model computational efficiencies, and poor convergence, limiting the application of mechanistic-model-based process optimization and advanced control in complex refining production processes. To address this problem and take advantage of big data technology, this study used case-based reasoning (CBR) for process optimization. The proposed method makes full use of previous process cases and reuses previous process cases to solve production optimization problems. The proposed process optimization method was applied to an actual fluid catalytic cracking maximizing iso-paraffins (MIP) production process for industrial validation. The results showed that the CBR method can be used to obtain optimization results under different optimization objectives, with a solution time not exceeding 1 s. The CBR method based on big data technology proposed in this study provides a feasible solution for fluid catalytic cracking to achieve online process optimization.

Automated summary

This study used case-based reasoning method and big data technology to solve optimization problems in complex refining production processes. The results show that this method can provide solutions under different optimization objectives in a short period of time, providing feasible solutions for online process optimization in fluid catalytic cracking.