Inferential Model Predictive Control of Continuous Pulping under Grade Transition
Hyun‐Kyu Choi, Sang Hwan Son, Joseph Sang‐Il Kwon
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.