Search
Use the search bar or filters below to find any TAPPI product or publication.
Filters
Content Type
Publications
Level of Knowledge
Committees
Event Type
Collections
Journal articles
Effect of xylan on the mechanical performance of softwood kraft pulp 2D papers and 3D foams, TAPPI Journal March 2025
ABSTRACT: Pulp fibers are paramount in paper products and have lately seen emerging use in fiber foams. Xylan, an integral component in pulp fibers, is known to contribute to paper strength, but its effect on the strength of pulp fiber foams remains less explored. In this study, we investigate the role of xylan in both 2D handsheets and 3D foams. For a softwood kraft pulp, we enzymatically removed 1% from pulp fibers and added 3% xylan to them by adsorption, corresponding to approximately a decrease of a tenth and an increase of a third of the total xylan content. The mechanical properties of 2D fiber networks, i.e., handsheets, made using the xylan-enriched pulp improved, particularly regarding tensile strength and Young’s modulus; however, the decrease in mechanical properties of handsheets made from enzymatically- treated xylan-depleted pulp was more pronounced. In 3D networks • pulp fiber foams, much less fiber-fiber contacts formed, and thus the mechanical properties were not as much influenced by removal of xylan. Furthermore, the presence of the required surfactant on the fibers, acting as debonding agent, overshadows any positive effect xylan might have on fiber-fiber bonding. We propose that the improved mechanical properties for the sheets result from a combination of an increased number of fiber-fiber bonds and higher sheet density, while the deterioration in mechanical properties of handsheets comprising enzymatically-treated fibers is caused by the opposite effect.
Journal articles
Predictive advisory solutions for chemistry management, control, and optimization, TAPPI Journal March 2025
ABSTRACT: Process runnability and end-product quality in paper and board making are often connected to chemistry. Typically, monitoring of the chemistry status is based on a few laboratory measurements and a limited number of online specific chemistry-related measurements. Therefore, mill personnel do not have real-time transparency of the chemistry related phenomena, which can cause production instability, including deposition, higher chemical consumption, quality issues in the end-product and runnability problems. Machine learning techniques have been used to establish soft sensor models and to detect abnormalities. Furthermore, these soft sensors prove to be most useful when combined with expert-driven interpretation. This study is aimed at utilizing a hybrid solution comprising chemistry and physics models and machine learning models for stabilizing chemistry-related processes in paper and board production. The principal idea is to combine chemistry/physics models and machine learning models in a fashion close to white box modeling. A cornerstone in the approach is to formulate explanations of the findings from the models; that is, to explain in plain text what the findings mean and how operational changes can mitigate the identified risks. The approach has been demonstrated for several different applications, including deposit control in the wet end, both raw water treatment and usage, and wastewater treatment. This approach provides mill personnel with knowledge of identified phenomena and recommendations on how to stabilize chemistry-related processes. Instead of using close to black box machine learning models, a hybrid solution including chemistry/physics models can enhance the performance of artificial intelligence (AI) deployed systems. A successful way of gaining the trust from mill personnel is by creating a plain text explanation of the findings from the hybrid models. The correlation between the likelihood of a phenomena and disturbance and the explanations are derived and validated by application and chemistry and physics experts.