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• A method for modeling and optimizing processes with limited data is proposed. • Three swarm intelligence algorithms are studied to develop the method. • SI-M achieves high accuracy and reasonable computational time for process modeling. • SI-M with SMA balances exploration ability and computational cost well. • SI-O maximizes productivity and ensures the continuous operation simultaneously. 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 Intelligence-based 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. [ABSTRACT FROM AUTHOR]