<![CDATA[Temperature control is crucial for maintaining stable and effective thermal treatment in pyrolysis system. For this application, Proportional-Integral-Derivative (PID) controller is frequently utilized due to its ease of use and efficiency. This study aims to evaluate and compare the performance of classical and metaheuristic tuning methods for PID controllers in pyrolysis temperature control. This work compares conventional Ziegler-Nichols (ZN) and Cohen-Coon (CC) methods with metaheuristic optimization techniques, specifically Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for tuning PID controller parameters. The main contribution of this research is the demonstration of improved control performance and computational efficiency using PSO-based PID tuning for pyrolysis applications. Simulation results show that PID controllers that the parameters tuned by GA and PSO achieve faster and smoother responses, with small overshoot, compared to classical methods. From both methods, PSO provides balanced performance with the shortest rise time (30.66 s), fastest settling time (50.80 s), and lowest overshoot (1.15%). Although both GA and PSO can maintain the set point of 500 °C with satisfactory transient response, PSO also shows better convergence efficiency, with smaller iteration numbers and lower computational effort. The results indicate that PSO-tuned PID is suitable for pyrolysis temperature control applications.]]>
Copyrights © 2026
