Evolutionary Based Control and Optimisation of Exothermic Batch Process

Control and optimization of exothermic batch reactions are crucial tasks in the field of chemical engineering, with extensive applications in various industries such as healthcare, monitoring, and production. This review comprehensively analyzes the state-of-the-art evolutionary algorithms used for controlling and optimizing exothermic batch reactions. The paper explores and compares the performance of popular algorithms such as GA, PSO, and other hybrid evolutionary algorithms. The review focuses on the strengths and limitations of each algorithm, analyzing their capabilities in handling different exothermic batch reaction processes, including their applicability, robustness, and complexity. Additionally, the paper investigates the commonly used optimisation techniques based on evolutionary algorithms for controlling exothermic batch reactions in practical case studies. This review also reveals the latest advancements and emerging trends in this field, such as multi-objective optimization, multi-mode optimization, and the integration of deep learning methods.