Inferential model predictive control of blow-line fiber morphology in a continuous pulp digester via multiscale modeling

Since the fiber morphology of cooked chips (e.g., cell wall thickness) determines paper mechanical properties, desired fiber morphologies need to be precisely achieved during pulping. In this work, a multiscale model for a continuous pulp digester is developed by integrating a well-known macroscopic model (i.e., extended Purdue model) and a microscopic model (i.e., kinetic Monte Carlo algorithm) to describe the spatiotemporal evolution of wood chips and cooking liquor during Kraft pulping. Then, an approximate model is identified to circumvent the high computational requirement of the proposed multiscale model, followed by designing a soft sensor to infer the primary outputs. Lastly, a model-based feedback controller is designed to regulate the blow-line Kappa number (i.e., residual lignin content at the bottom of the continuous pulp digester) and cell wall thickness to desired values.