Genetic Algorithm-Optimized LQR Control Strategy for Boosting Ethanol Production and Minimizing Control Effort

This paper presents a novel control strategy for the dynamic optimization of ethanol fermentation using a Linear Quadratic Regulator (LQR) whose gains are tuned via Genetic Algorithm (GA). The fermentation process, characterized by nonlinear and time-varying dynamics, demands advanced control strategies for optimal performance to maximize the production of ethanol and to minimize its environmental impact. A procedure for controlling the bioreactor's temperature is created because it is a crucial component of an effective process. Multiple evaluations conducted using genuine computational simulations are provided to confirm the efficacy of controlling techniques. A GA is employed to optimize the LQR's cost function matrices Q and R, aiming to minimize a composite performance index based on tracking error and control energy. The fitness function evaluates each candidate based on performance of various criteria such as Integration of Absolute Errors (IAE), Integral Time Squared Error (ITSE), Integral Time Absolute Error (ITAE), and Integration of Square Errors (ISE). The simulation results demonstrate that the GA-optimized LQR outperforms conventionally tuned controllers in terms of ethanol yield, disturbance rejection, and system stability. This approach shows promise for scalable implementation in industrial biochemical process control.