In the Kraft pulping process, the cell wall thickness (CWT) of wood chips significantly affects paper properties. This work proposes a novel multiscale model by combining the mass continuity and thermal energy balance equations adopted from a modified "extended Purdue model" with a kinetic Monte Carlo algorithm to describe the microscopic events such as the evolution of CWT and Kappa number; otherwise, these microscopic properties of wood chips are not accessible by existing measurement techniques. To reduce the model complexity, a data-driven reduced-order model is developed, which is then used for developing a soft sensing system (i.e., Kalman filter) to estimate the primary measurements (e.g., CWT, Kappa number) by utilizing the secondary measurements (e.g., active effective alkali and dissolved lignin concentrations in the free-liquor phase). Based on this soft sensing system, a model-based feedback control framework is developed to regulate both the CWT and Kappa number of wood chips. Published by Elsevier Ltd.