Genetic Algorithm-Based PID Optimization for Ethyl Acetate Saponification in a Continuous Stirred Tank Reactor

Effective temperature control in continuous stirred-tank reactors (CSTRs) is essential for maintaining product quality and process stability in nonlinear chemical systems. Traditional PID controllers, tuned via Ziegler-Nichols (ZN) methods, often struggle to manage the nonlinearities of such systems, leading to high overshoot, prolonged settling times, and suboptimal disturbance rejection. This study introduces a genetic algorithm (GA)-based approach for optimizing PID controller parameters to enhance the performance of temperature control during the saponification of ethyl acetate in a CSTR, a mildly exothermic reaction characterized by second-order kinetics. The proposed method employs the integral of time-weighted absolute error (ITAE) as a fitness function to iteratively minimize system error and optimize controller gains. Comparative analysis with the ZN-tuned PID controller reveals substantial improvements using the GA-tuned PID controller, including a reduction in overshoot from 61.4% to 38.1%, and decreases in rise, peak, and settling times by 29.7%, 35.3%, and 72.02%, respectively. Additionally, the GA-PID controller demonstrates superior set-point tracking and robust disturbance rejection, achieving a system error reduction of 68.1% compared to the ZN-PID controller. These results underscore the efficacy of genetic algorithms in overcoming the limitations of conventional tuning methods for nonlinear systems. The GA-based tuning approach not only enhances control accuracy and stability but also offers a scalable solution for optimizing complex industrial processes, paving the way for advancements in chemical reactor control and broader applications in process engineering.