An experimental study for property control in a continuous styrene polymerization reactor using a polynomial ARMA model

By using a multivariable nonlinear model predictive controller (NLMPC), the control experiments for the monomer conversion and the weight-average molecular weight are conducted in a continuous styrene polymerization reactor. Instead of a complex first-principles model, a polynomial auto-regressive moving average model (ARMA) is used to describe the nonlinear behavior of the polymerization reactor. The pseudorandom multilevel input signals mounted on the jacket inlet temperature and the feed flow rate are applied to the polymerization reaction system to identify a polynomial ARMA model. In the experiments of identification and control, the monomer conversion and the weight-average molecular weight are measured by on-line densitometer and viscometer with appropriate correlations. The on-line measurements are found to be in good agreement with the off-line analysis by the gravimetry and the gel permeation chromatography. Since a polynomial ARMA model is expected to give a higher order objective function of input variables, we employ the extended Kalman filter based NLMPC scheme to reduce the computational requirement in the control experiments. The NLMPC based on the polynomial ARMA model is found to perform satisfactorily for the control of the polymer properties during a grade-transition period as well as under the steady-state operation.