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Journal articles
Gap mechanics in pulp refiners, TAPPI Journal June 2025
ABSTRACT: Studies of pulp refining have shown that a single bar impact on pulp has only a 1%•5% probability of producing a successful refining effect. This study has explored the reason why. An analysis of refining kinetics suggested that small segments of a fiber length, about a fiber diameter in size, are treated during each impact. Measurements of localized swelling along fiber lengths caused by refining supported this finding. Based on these findings, it was postulated that force transmittal through fiber networks occurred primarily at fiber crossings. The small size of fiber diameters relative to fiber lengths accounts for the low probability of a successful refining event at each impact. This probability, and the probability of fibers being captured and impacted during passage through a refiner, account for the need for multiple bar crossings to refine pulps.
Journal articles
Improved barrier performance with microfibrillated cellulose, TAPPI Journal March 2025
ABSTRACT: In this work, the impact of microfibrillated cellulose (MFC) on the properties of water-based barrier coatings intended for food packaging have been explored. Commercially available MFC was used for improving the rheology and water retention of three different commercially available dispersion coatings (acrylic, styrene acrylic, and polylactic acid). Coatings were applied by rod to paper, and barrier properties were tested by measuring air permeability and water barrier properties. Results clearly showed that addition of MFC to water-based dispersion coatings improved the barrier performance of the final coatings.
Journal articles
Application of AI-based approach to control the papermaking process, TAPPI Journal March 2025
ABSTRACT: This paper explores AI’s role in revolutionizing the pulp and paper industry, and specifically in predicting wet tensile strength (WTS) for specialty-grade papers. Leveraging eLIXA technology, a 90-day study achieved a 15% reduction in chemical dosage and an 80% decrease in wet tensile standard deviation. The real-time dosage prediction led to optimizing the wet strength resin (WSR) consumption and improved process reliability. The self-learning models exhibited adaptability to changing variables, ensuring their robustness. Overall, this study highlights AI’s transformative impact on efficiency, cost savings, and product quality within the dynamic landscape of papermaking. The approach used for wet strength optimization has been used to optimize other aspects of pulp and paper production.