(610e) Mitigation of Fiber Degradation in a Pulp Digester Via Multiscale Modeling and Control of the Degree of Polymerization of Cellulose

Due to the growing interest in sustainability, pulp products have gained attention as renewable alternatives to non-renewable materials such as plastics. [1]. For instance, over the past ten years, the demand for packaging paper such as paperboard has increased by 32 %, which counterbalances the reduced production of copy paper [2]. Hence, such increasing demand for pulp products has prompted the development of an efficient control strategy to produce pulps with desired grades such as brightness and strength while minimizing the ratio of off-spec pulp products [3]. Moreover, the operating conditions of the pulping process, such as a cooking temperature and cooking liquor dosage [4], should also be considered to achieve target pulp properties.

Specifically, cellulose degree of polymerization (DP), which is a cellulose chain length in pulp, has been known to play a crucial role in determining the tensile strength of end-use papers [5, 6]. Hence, it is important to understand the time-varying characteristics of DP during the process [7 - 9]. For instance, during the industrial pulping process, such as Kraft pulping, DP significantly decreases due to cellulose chain break induced by alkaline hydrolysis (i.e. fiber degradation). However, the direct measurement of DP during delignification is unavailable because of practical issues such as measurement delay and limited sensor placements [10]. Moreover, even though several efforts have been made to develop first principle models to capture macroscopic phenomena in a pulp digester, the nanoscale events could not properly be elucidated since the previously developed deterministic models are not able to describe the microscopic variables such as the cellulose chain scissions at random locations, which are governed by stochastic events.

Motivated by these limitations, in this work, a multiscale model was developed to capture the multidimensional phenomena which are simultaneously taking place at different time and length scales in a pulp digester. First, the Purdue model, which is a widely used mathematical model for pulping process, and a kinetic Monte Carlo (kMC) algorithm were integrated to capture the evolution of both the Kappa number (i.e., residual lignin content in pulps) and fiber morphology such as cell wall thickness (CWT) and cellulose DP [11 - 13]. Subsequently, separate kMC events were employed in the simulation to handle the scale differences between microscopic events. Thereafter, reaction parameters were estimated, and the developed multiscale model was validated by experimental data sets including cellulose DP and lignin content. The developed model was proven to have predictive capability under varying pulping conditions (e.g., cooking time, temperature, and inlet white liquor concentration) and intensified feedstock variability (e.g., fiber-to-fiber heterogeneity and feed fluctuation). The developed model was then incorporated into the model-based controller to obtain the optimal input sequences to attain the target pulp properties such as Kappa number while mitigating fiber degradation during the pulping process. Consequently, the designed model-based feedback controller enables the pulp properties to be driven to set-point values with the minimum usage of energy and alkaline cooking liquor.

References

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[13] Choi, H. K., & Kwon, J. S. I. (2020). Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester. AIChE Journal, 66(7), e16972.