Dynamic Of Batch Chemical Processes Using Intelligent Strategies: A Review

Abstract

Batch reactors are extensively used in chemical industries because of their adaptability and superior product quality; however, their nonlinear and time-varying characteristics present considerable challenges for dynamic modeling and control. Conventional modeling methods frequently inadequately represent intricate behaviors. This review examines the contribution of artificial intelligence (AI) techniques—specifically machine learning (ML), neural networks (NN), and reinforcement learning (RL)—to the improvement of modeling, prediction, and control in batch chemical processes. The primary objective is to assess and compare these AI methodologies, discover their strengths and shortcomings, and underline their value in industrial applications. Principal findings indicate that AI-driven strategies greatly enhance performance, adaptability, and optimization in batch systems. The study addresses challenges including data requirements and computational demands and suggests future directions such as hybrid AI frameworks and real-time optimization. This study seeks to direct researchers and practitioners toward enhanced and effective batch process management