Reduced order methodologies for the control of particle size distribution in emulsion polymerization

Emulsion polymerization has significant advantages over bulk and solution polymerization processes. These advantages result mostly from the multiphase and compartmentalized nature of the emulsion polymerization, delivering a high versatility to product qualities but adding to the complexity of the process. The control of the full particle-size distribution (PSD) in emulsion polymerization is vital for industrial applications where the target distributions are usually complex and/or multimodal. Despite the strong need, control of the full PSD presents a challenging task mainly due to the complexity of emulsion polymerization and the lack of adequate on-line measurement instrumentation. In this work, in-batch and batch-to-batch control strategies are developed for the regulation of the full PSD in semibatch emulsion copolymerization of Vinyl Acetate/Butyl Acrylate with nonionic surfactants and a redox initiator pair. These strategies are also applicable for regulation of distributions in other particulate systems governed by population balances.;PID controllers are employed for regulating nucleation and growth events through tracking the nominal trajectories of total number of particles and the solids content by manipulating the flowrates of the more reactive monomer, butyl acrylate, and the surfactant. The second control strategy is based on tracking nominal trajectories of the moments of the distribution rather than the distribution itself with a model predictive controller (MPC). In a more sophisticated approach, a nonlinear model predictive controller is designed utilizing the detailed population balance model of the system. The ill-conditioning and the high-dimensionality of the resulting dynamical system is removed by principal component analysis (PCA)-based model order reduction. The large time delay associated with PSD measurements is compensated by an optimal multi-rate filter that enables the MPC to utilize the fast density measurements. A cascade control architecture is proposed for rejecting disturbances that may be experienced during the batch. This architecture relies on the fact that control of nucleation and growth kernels throughout the batch will result in effective regulation of the PSD. An advanced master controller determines the reference trajectories for nucleation and growth kernels. A slave controller manipulates the feedrates to track the updated kernel setpoints. In the final study, a batch-to-batch controller incorporating in-batch on-line measurements is designed and validated experimentally. The controller utilizes a partial least squares model of the system to predict the future behavior of the system and correct for persistent disturbances.