Article Details

Abstract

The Continuously Stirred Tank Reactor (CSTR) serves as a core reaction unit in chemical process industries, and its operational control performance directly affects the quality of final products and production stability. Due to the nonlinearities, large time delays, time-varying characteristics, and complex reaction mechanisms of CSTR, traditional control methods struggle to satisfy actual production requirements. To address this issue, a data-driven control approach—Model-Free Adaptive Control (MFAC)—is adopted to regulate the discharge temperature of the CSTR system. First, a state-space simulation model of the reactor is established in the Simulink environment. Then, to improve MFAC performance, an improved Compact-Form Model-Free Adaptive Control (iCF-MFAC) method is employed to design the controller, and particle swarm optimization is adopted for parameter optimization. Simulation results demonstrate that, compared with conventional MFAC, the proposed method significantly improves the dynamic performance of the system, achieves zero overshoot and fast response, enhances control accuracy, thereby verifying the effectiveness and feasibility of the proposed method.

Keywords

reactor; CSTR; particle swarm optimization; Model-Free Adaptive Control; temperature control

References

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Funding

2024 Academy level Project Funding (JATC24010113)

About the authors

Chen Lu (1998-), female, Han nationality, from Xuzhou, Jiangsu, teaching assistant, master's degree, research direction: intelligent control algorithm research.