The interest in emulsion polymerization is strong because it offers the possibility of producing products with special properties not easily produced by other types of polymerization processes. Because the main reaction medium is water, not an organic solvent, its importance is steadily increasing with efforts to design environmentally benign processes. The control of emulsion polymerization is a challenging problem due to major limitations encountered in its modeling and on-line monitoring. Although emulsion polymerization has been studied and used for several decades, progress has been slow. Industrial practice relies heavily on experience white several controversial issues are stilt being studied. The literature related to this issue is vast and sometimes contradictory, and most deals with experimental investigation of specific emulsion polymerization systems to improve the process understanding and develop a process model. However, universally accepted conclusions are scarce. This article reviews the major issues related to control of emulsion polymerization. The latest contributions in process understanding, mathematical modeling, sensor technology, and process control techniques for emulsion polymerization are discussed. Emphasis is on contributions, which are important in resolving the control of emulsion polymerization processes.

Measurement of material moisture content is necessary for the control of product quality in batch drying. However, this variable cannot be measured on-line, and state estimation techniques are proposed. A non-linear dynamic model is developed for batch drying of foods. Process disturbances and measurement errors are modeled as stochastic processes and a hybrid extended Kalman filter is employed for state estimation. This filter is based on the local linearization of the process model around the suboptimal filter estimates. The moisture estimation approach was applied to experimental points obtained in a laboratory dryer with quite satisfactory results.

Monitoring and control of emulsion polymerization reactors presents several difficulties because of profound modeling limitations and on-line measurement problems. The use of Kalman filtering to obtain optimal estimates of the process states in the presence of modeling inaccuracies, process disturbances and limited measurements is investigated. Different filter designs were developed and experimentally tested. Adaptive filtering was found to be extremely useful in tracking time-varying model parameters of large uncertainty and more robust in the case of unknown process noise statistics. The model used in all Kalman filter designs describes accurately the polymerization kinetics but grossly oversimplifies the thermodynamic relationships for the monomer distribution between the aqueous and polymer phases as well as the time dependency of the average number of radicals per particle. Despite this apparent process/model mismatch acceptable estimates of the process states were obtained.

AbstractThe appearance of pseudo‐steady states in semicontinuous emulsion copolymerization is studied. Different correlations between the monomer feed rate and the polymerization reaction rate proposed in the literature for homopolymerization systems can be derived from a unified view as presented in this work. The theoretical analysis for copolymerization systems predicts that the individual consumption rates of both monomers involved assume a constant value if the monomer addition rates are kept constant. Different cases for either fixed radical concentration or seeded polymerization with water‐soluble or sparingly water‐soluble monomers are considered. The assumptions and results of the analysis are tested against experimental data from the seeded copolymerization of vinyl acetate/n‐butyl acrylate.

The application of Kalman filtering theory to address the problem of composition control in emulsion copolymerization is investigated. Two different filter designs were implemented carrying out standard state and noise-adaptive state estimation. Satisfactory digital monitoring of the process was obtained with both schemes. The filter estimates were utilized by a deterministic controller regulating the monomer addition rates of the two monomers into a reactor operating in semicontinuous fashion. The quality control strategy was based on a model developed for the vinyl acetate (VAc)/ butyl acrylate (BuA) comonomer system. Real-time experiments demonstrated the efficiency of the approach to minimize drastically the compositional drift.

The development of a nonlinear dynamic model of the seeded semicontinuous emulsion copolymerization of vinyl-acetate-n-butyl acrylate for copolymer composition control is summarized. Because the available process measurements do not yield the entire state vector directly, nonlinear state estimation is used. Process disturbances and measurement errors are modeled as stochastic processes and a hybrid extended Kalman filter is employed for state estimation. The filter is based on the local linearization of the process model around the suboptimal filter estimates which are needed for the effective composition control of the produced copolymer.

Composition control of the sub-micron latex particles in the semicontinuous emulsion copolymerization of vinyl acetate / n-butyl acrylate is investigated. A stochastic approach is employed to compensate for modeling inaccuracies and measurement errors and an adaptive Kalman filter is used to estimate simultaneously the process states and the statistics of the noise vectors. A combined feedforward/feedback controller is derived to maintain constant copolymer composition in the latex particles. The controller operates on the Kalman filter estimates and manipulates the monomer addition rates to the polymer reactor.