Model-based predictive control studies for a continuous pulp digester

As various industries continue to develop complex, fundamental process models, there exists a need to systematically incorporate these complex models into the controller design. Three model predictive controllers (MPG), each incorporating internal models with varying degrees of complexity, are applied to a nonlinear, fundamental, continuous pulp digester "plant." The first two controllers utilize linear models, one obtained through subspace identification and the other obtained from the linearization of the fundamental model. The third model predictive controller uses the complex, nonlinear digester model with extended linearization to update the controller model for future predictions and control computations. The two MPC controllers based on the fundamental model, both linear and nonlinear, had better closed-loop performance than the controller utilizing the subspace identified model. The closed-loop performance of the linear and nonlinear MPC controllers (based on the fundamental model) were indistinguishable for stochastic disturbance rejection.