Review of Control Strategies for Batch Polymerization Reactors

Batch polymerization reactors exhibit strong nonlinear behavior, time-varying dynamics, and significant uncertainties arising from complex reaction mechanisms, gel and glass effects, and heat and mass transfer limitations. These characteristics make effective temperature regulation and product quality control particularly challenging using conventional control strategies. This review presents a comprehensive overview of modeling, estimation, and control approaches developed for batch polymerization processes, with a primary focus on methyl methacrylate–based systems. Early efforts addressing diffusion limitations and viscosity effects through mechanistic modeling are discussed, followed by nonlinear control techniques such as globally linearizing control and observer-based methods. The review further examines the evolution of data-driven and adaptive control strategies, including neural networks, recursive least squares–based identification, model predictive control, and hybrid adaptive–predictive frameworks. Comparative analyses reported in the literature highlight the trade-offs between advanced model-based controllers and classical PID schemes in terms of robustness, computational complexity, and economic feasibility. Emphasis is placed on adaptive control methodologies as a practical solution to handle model uncertainties and rapidly varying process dynamics. Finally, key implementation challenges and future research directions aimed at bridging the gap between advanced control theory and industrial batch polymerization applications are identified.