Abstract
Abstract
Accurate temperature regulation in nonlinear continuous stirred tank reactors (CSTRs) remains a challenging task due to strong nonlinearities and operating-point sensitivity. Although numerous proportional-integral-derivative (PID) tuning approaches have been proposed, most existing studies primarily focus on nominal operating conditions, often resulting in degraded performance under varying process dynamics. To address this limitation, this study proposes an optimization-based PID with filter (PIDf) tuning framework that enhances consistency and reliability across different operating scenarios. A global-guided search mechanism is incorporated into the optimization process to improve convergence stability and solution quality without increasing computational complexity. The proposed framework is evaluated on a nonlinear jacketed CSTR system under setpoint variations and multiple operating conditions. Its performance is benchmarked against recent metaheuristic optimization methods and classical tuning strategies using time-domain specifications and error-based performance indices. The results indicate that the proposed approach achieves faster settling behavior, reduced overshoot, and improved consistency, while maintaining stable performance across repeated runs. Overall, the performance of the proposed approach is quantitatively assessed using standard time-domain and error-based metrics, providing a systematic evaluation of control quality. These findings highlight the applicability of the proposed framework for temperature regulation in nonlinear chemical processes.