(149g) Multiscale Kinetic Modeling and Optimization: In-Depth Analysis of Cellulose Degradation for Enhanced Pulping Process and Superior Paper Quality

Recently, despite advancements in information technology (IT), the demand for paper products remains remarkably high and continues to increase. This trend is primarily driven by the rapid growth of e-commerce businesses and their extensive need for packaging materials [1]. In response to environmental concerns, such as carbon neutrality, the paper industry has made significant endeavors to reduce its carbon footprint. These efforts involve optimizing resource consumption [2] and producing mechanically superior papers that can be easily recycled after use [3]. A thorough understanding of the pulping process is critical for further optimization.

Understanding how the underlying structure of the wood chips evolves in a pulp digester and identifying factors that contribute to paper quality are essential. Cellulose, derived from woody biomass, serves as the primary building block of paper products and functions as the load-bearing element of paper [4, 5]. To produce mechanically strong papers, it is necessary to maintain a high degree of polymerization (DP) in the cellulose microfibers [6]. Additionally, low lignin content in the resulting cellulose fibers is desirable for high-quality papers [7]. However, current pulping practices using strong chemical reagents effectively remove lignin while also causing unavoidable cellulose degradation, creating a trade-off. Therefore, it is crucial to control operating conditions such as reaction temperature and time [8]. Nonetheless, tracking cellulose DP and lignin content during the pulping process remains a challenge, making the study of the relationship between operating conditions and cellulose quality a paramount interest. Establishing an appropriate kinetic model during pulping is difficult, as the bulk dissolution and depolymerization of cellulose fibers occur simultaneously but at different length and time scales.

Motivated by this challenge, we propose a multiscale model employing a kinetic Monte Carlo (kMC) approach with a simulation lattice to track the spatiotemporal evolution of the biomass in the reaction system. We implemented several layers to describe cellulose dynamics: a macroscopic layer for the continuum model (mass/energy balances); a mesoscopic layer coupled with the kMC simulation lattice (dissolution of components); and a microscopic layer for cellulose depolymerization. This approach enabled us to obtain mesoscopic properties such as Kappa number (quantifying lignin content) and cell wall thickness (CWT), as well as microscopic properties like cellulose DP. Notably, our kinetic model classifies the cellulose degradation mechanism into two categories: dissolution and depolymerization, corresponding to meso/microscopic scales, respectively.

We verified the multiscale model using experimental results from lab-scale pulping with phenol-4-sulfonic acid (PSA). Since the process outputs are sensitive to operating conditions, including solvent concentration and temperature, we also performed sensitivity analyses. Also, we implemented the proposed model to develop optimal operating strategies for achieving target properties in pulp products. Specifically, we developed a model predictive control (MPC) to track set-points while satisfying the process constraints. The MPC had two objectives: Kappa number and cellulose DP, with manipulated inputs being cooking time, temperature, and PSA concentration. The closed-loop simulations demonstrated that the required set-points are attained at the end of the operations.

Literature cited:

[1] Mboowa D. (2021). A review of the traditional pulping methods and the recent improvements in the pulping processes. Biomass Conv. Bioref., 1, 1-12.

[2] Kermani M., Perin-Levasseur Z., Benali M., Savulescu L., & Marechal F. (2017). A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Krapt pulping mill. Comput. Chem. Eng., 102, 238-257.

[3] Sridach W., (2010). The environmentally benign pulping process of non-wood fibers. Suranaree J. Sci. Technol., 17, 2.

[4] Chen J., Zhang M., Yuan Z., & Wang J. (2013). Improved high-tield pulp network and paper sheet properties by the addition of fines. BioResources, 8, 6309-6322.

[5] Lavrykov S., Ramarao B., Lindstrom S., & Singh K. (2012). 3D network simulations of paper structure, Nord. Pulp Pap. Res. J., 27, 256-263.

[6] Jung J., Choi H.-K., Son S.H., Kwon J.S.-I., & Lee J.H. (2022). Multiscale modeling of fiber deformation: Application to a batch pulp digester for model predictive control of fiber strength. Comput. Chem. Eng., 158, 107640.

[7] Son S.H., Choi H.-K., & Kwon J.S.-I. (2021). Application of offset-free Koopman-based model predictive control to a batch pulp digester. AIChE J., 67, e17301.

[8] Son S.H., Choi H.-K., Moon J. & Kwon J.S.-I. (2022). Hybrid Koopman model predictive control of nonlinear systems using multiple EDMD models: An application to a batch pulp digester with feed fluctuation, Control Eng. Pract., 118, 104956.