ARTIFICIAL INTELLIGENCE FEATURES ON OBSERVATIONS OF NONLINEAR CHEMICAL REACTOR DYNAMICAL PROCESS (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2025)
This paper proposes a novel approach for enhancing the optimization of nonlinear high-gain observers by utilizing a genetic algorithm (GA) to improve state estimation precision in chemical reactors. Unlike traditional tuning methods, the GA optimally seeks optimal observer gain parameters that yield a minimum estimation error and improve convergence rates. The new method is used to benchmark a nonlinear continuous stirred-tank reactor (CSTR) model. The simulation outcomes validate that the GA-optimized observer exhibits a substantially enhanced rate of convergence and accuracy in estimating the temperature and concentration states compared to traditional methods. Additionally, the technique enables smaller dependence on physical sensors, thus promoting stronger and less expensive monitoring and control systems. The approach introduced is model-independent and applicable in real-time to an extensive class of engineering systems, including electrical and power systems. This work highlights the practical benefits of integrating metaheuristic optimization and nonlinear observer design in industrial processes.
Keywords: High-gain observers; Chemical reactor; Mathematical models; Artificial intelligence (AI); Genetic algorithm (GA)