Subspace identification based inferential control of a continuous pulp digester

Abstract

In this paper we develop an inferential control scheme for Kappa Number control in a continuous pulp digester. First, a state-space model that correlates the Kappa number to an optimally selected set of liquor measurements is identified from input output data using a subspace identification technique. The model is then used to construct a multi-rate Kalman filter and a model predictive controller. Simulation results are provided to illustrate the efficacy of the approach.