Processes 2023 , 11 , 809
2of 19
One potential solution is to mitigate fiber degradation through strength modeling. By meeting the specific standards of paper, the amount of waste generated can be signifi- cantly reduced. However, achieving the strength requirements and properties of different paper products can be challenging as each product has its own unique strength standard. The tensile strength of paper is an important strength property, and cellulose is responsible for forming crystalline microfibers with excellent tensile strength [14]. The cellulose degree of polymerization ( DP ) is a measure of the cellulose chain length and is the major factor determining the tensile strength of the product as cellulose is the load-bearing element of a pulp fiber [15,16]. Therefore, a good paper product has a high cellulose DP , which represents high strength, and a low Kappa number, which represents a brighter paper. As the final paper product properties, such as strength and yield, are primarily determined by metrics, such as the Kappa number and DP , it is crucial to optimize the pulping process to achieve these specific properties with desired values.
Figure1. Worldwide paper consumption prediction volume.
Kraft pulping is the most commonly used chemical pulping process, accounting for 80% of chemical pulp produced in the United States [17]. During kraft pulping, wood chips are exposed to an aqueous solution, which removes both lignin and hemicellulose. Strong alkaline solvents, such as sodium hydroxide (NaOH), are added to the solution to increase the degree of delignification (i.e., to decrease the Kappa number). However, this also results in the breakdown or degradation of cellulose to glucose [18]. To prevent cellulose degradation and obtain the desired paper properties, it is essential to control the operating conditions, such as the batch cooking time, NaOH concentration, and tem- perature. There have been several approaches in the literature to develop macroscopic and mesoscopic computational models with the extended Purdue model being the most commonly used [19–22]. However, these models only consider macroscopic equations. They cannot predict important microscopic properties, such as the Kappa number, fiber morphology, DP , and cell-wall thickness (CWT). Recent studies [6,7,23,24] have developed multiscale models using kinetic Monte Carlo (kMC) algorithms [25,26] to describe these microscopic properties and understand fiber-to-fiber heterogeneity. They also designed a model-based controller using reduced-order models and Kalman filters [27,28]. However, these studies did not
Made with FlippingBook Digital Publishing Software