TAPPI Journal Archive
About TAPPI Journal
As of March 2022, TAPPI Journal’s (TJ) publishing model is 100% Open Access (OA) to improve the accessibility of its published articles, increase researcher engagement and make research more visible. This new format helps researchers meet their funding and grant application requirements and potentially increase the number of citations. As in the past, the copyright remains with the author, and unlike other technical journals, TJ does not require a publication fee. Read more.
Editorial: The emergence of AI in additives development, TAPPI Journal March 2025
March 23, 2025
ABSTRACT: The continuing evolution of artificial intelligence (AI) and its penetration into the core of the world of papermaking were undeniable at TAPPICon 2024 and especially within the content presented and sponsored by TAPPI’s Papermaking Additives Committee. On one side of the spectrum, there were traditional methods of chemical development and application grounded in natural intelligence, while on the other, there was the emerging presence of algorithmic decision-making and machine learning within the development cycle. The latter technology is brimming with the kind of promise that could reshape how additives are conceived, developed, and applied, turning what was once a matter of trial and error into something far more precise and previously out of reach.
Effect of xylan on the mechanical performance of softwood kraft pulp 2D papers and 3D foams, TAPPI Journal March 2025
March 23, 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.
Improved barrier performance with microfibrillated cellulose, TAPPI Journal March 2025
March 23, 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.
Using multi-method analysis to identify challenging paper machine deposits and defects, TAPPI Journal March 2025
March 23, 2025
ABSTRACT: Based on its speed and versatility, Fourier transform infrared (FTIR) spectroscopy is the industry’s common starting point for analysis of a paper machine deposit or defect sample. However, certain contaminants and papermaking process additives cannot be precisely identified solely by infrared spectral interpretation. This lack of specificity could lead to a misinterpretation of the composition of the deposit or defect. A multi-method analysis uses data from two or more analytical techniques, including FTIR spectroscopy, microbiological staining/phase contrast microscopy, pyrolysis-gas chromatography/mass spectrometry (Pyro-GC/MS), and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX), to produce a more specific assessment of a sample’s composition. This paper discusses the use of a multi-method analysis in deposit and defect analysis and presents several case studies that demonstrate how this comprehensive approach can often produce an interpretation result of greater conviction and value to the papermaker.
Predictive advisory solutions for chemistry management, control, and optimization, TAPPI Journal March 2025
March 23, 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.
Application of AI-based approach to control the papermaking process, TAPPI Journal March 2025
March 23, 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.
