SI-M/O: Swarm Intelligence-based Modeling and Optimization of complex synthesis reaction processes

Processes with unknown reactions and limited data are commonplace in the chemical industry. However, modeling and optimizing these processes are challenging tasks. In this paper, we propose the Swarm Intelligencebased Modeling and Optimization (SI-M/O) algorithm to address these challenges. The SI-M/O algorithm integrates swarm intelligence with chemical process fundamentals. This fusion empowers SI-M/O to navigate complex chemical landscapes effectively. Swarm intelligence excels at exploring vast solution spaces and adapting dynamically. When combined with first-principle knowledge of chemical reactions and thermodynamics, SI-M/O not only finds optimal solutions but also considers chemical feasibility and physical constraints. To validate its effectiveness, we applied SI-M/O to optimize a production plant, achieving a substantial 5.3 % productivity increase during preliminary testing. We also designed a user-friendly graphical interface for SI-M/O, enhancing accessibility for researchers and practitioners.

Automated summary

This paper introduces a Swarm Intelligence-based Modeling and Optimization (SI-M/O) algorithm to address the challenges of unknown reactions and limited data in the chemical industry. The algorithm combines swarm intelligence with chemical process fundamentals, enabling it to effectively navigate complex chemical environments. By integrating first-principle knowledge of chemical reactions and thermodynamics, SI-M/O not only finds optimal solutions, but also considers chemical feasibility and physical constraints.