Inferential Model Predictive Control of Continuous Pulping under Grade Transition

Abstract

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.