Even though continuous pulp processes have been studied for many
years, the absence of a model that can accurately describe the evolution
of fiber morphology has impeded the application of advanced control
techniques. In this study, a multiscale model for continuous Kraft
pulping processes, which can capture the spatiotemporal evolution
of wood chips and cooking liquor, is developed by integrating a macroscopic
model (i.e., Purdue model) with a microscopic model (i.e., kinetic
Monte Carlo algorithm). Then, an approximate model is identified to
circumvent the high computational requirement of the multiscale model
and to handle the input time-delay, followed by designing a soft sensor
to infer state variables and primary measurements. This allows the
use of an inferential model predictive control strategy in a continuous
pulp digester to regulate the blow-line pulp properties (i.e., Kappa
number and cell wall thickness) and achieve optimal grade transitions.