Optimal Temperature Control for Non-linear Jacketed Continuous Stirred Tank Reactors using Genetic Algorithm
DOI:
https://doi.org/10.54388/jkues.v4i1.329Keywords:
PID Controller, CSTR, Ziegler-Nichols, Genetic Algorithm, Tuning , OptimizationAbstract
In this study, PID controller parameters were optimized for temperature control in a continuous stirred tank reactor (CSTR) during ethyl acetate saponification. Ziegler-Nichols (ZN) and ultimate parameters was initialized as boundary constrains to genetic algorithm (GA). A SIMULINK model was developed to simulate and optimize PID parameters by minimizing the Integral of Square Error (ISE) index, which measures system performance. The performance of the GA-tuned PID controller is compared with the conventional ZN method. The GA-PID controller achieves rapid stabilization and improved set-point tracking and disturbance rejection, demonstrating its effectiveness in minimizing system errors and enhancing overall control accuracy. These improvements are especially critical in handling the nonlinearities and dynamic complexities of CSTR operations. By reducing the ISE index, the GA-based tuning approach ensures more precise control and robust performance across varying operational conditions. This research highlights the potential of genetic algorithms as a superior alternative for PID tuning in complex chemical processes, offering enhanced system stability, product quality, and process efficiency compared to traditional methods. Results demonstrate that the GA-based PID controller significantly outperforms the ZN-PID controller. Specifically, the GA-PID decreased overshoot from 61.4% to 25.4%, and reduced settling and rise times by 67.88% and 43.48%, respectively.
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