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A novel predictive method for filler coflocculation with cellulose microfibrils, TAPPI Journal November 2019

ABSTRACT: Different strategies aimed at reducing the negative impact of fillers on paper strength have been the objective of many studies during the past few decades. Some new strategies have even been patented or commercialized, yet a complete study on the behavior of the filler flocs and their effect on retention, drainage, and formation has not been found in literature. This type of research on fillers is often limited by difficulties in simulating high levels of shear at laboratory scale similar to those at mill scale. To address this challenge, a combination of techniques was used to compare preflocculation (i.e., filler is flocculated before addition to the pulp) with coflocculation strategies (i.e., filler is mixed with a binder and flocculated before addition to the pulp). The effect on filler and fiber flocs size was studied in a pilot flow loop using focal beam reflectance measurement (FBRM) and image analysis. Flocs obtained with cationic polyacrylamide (CPAM) and benonite were shown to have similar shear resistance with both strategies, whereas cationic starch (CS) was clearly more advantageous when coflocculation strategy was used. The effect of flocculation strategy on drainage rate, STFI formation, ash retention, and standard strength properties was measured. Coflocculation of filler with CPAM plus bentonite or CS showed promising results and produced sheets with high strength but had a negative impact on wire dewatering, opening a door for further optimization.

Lignin-based resins for kraft paper applications, TAPPI Journal November 2019

ABSTRACT: We investigated miscanthus (MS) and willow (W) lignin-furfural based resins as potential reinforce-ment agents on softwood and hardwood kraft paper. These resins might be sustainable alternatives to the commercial phenolformaldehyde (PF) resins. Phenol is a petrochemical product and formaldehyde has been classified as a carcinogen by the U.S. Environmental Protection Agency. The lignin used in this study was derived from hot water extraction (160ºC, 2 h) of MS and W biomass, and may be considered sulfur-free. These biorefinery lignins were characterized for their chemical composition and inherent properties via wet chemistry and instrumental techniques. The resin blends (MS-resin and W-resin) were characterized for their molecular weight, thermal behavior, and mechanical properties. Mechanical properties were measured by the resin’s ability to reinforce softwood and hard-wood kraft papers. The effect of adding hexamethylenetetramine (HMTA), a curing agent, to the resin was also examined. Mixtures of PF and lignin-based resins were investigated to further explore ways to reduce use of non-renewables, phenol, and carcinogenic formaldehyde. The results show that lignin-based resins have the potential to replace PF resins in kraft paper applications. For softwood paper, the highest strength was achieved using W-resin, without HMTA (2.5 times greater than PF with HMTA). For hardwood paper, MS-resin with HMTA gave the highest strength (2.3 times higher than PF with HMTA). The lignin-based resins, without HMTA, also yielded mechanical properties comparable to PF with HMTA.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning, TAPPI Journal November 2019

ABSTRACT: Packaging manufacturers are challenged to achieve consistent strength targets and maximize pro-duction while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning.The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under ever-changing machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Effects of localized environment on eucalyptus clone chemica

Effects of localized environment on eucalyptus clone chemical composition, TAPPI JOURNAL September 2016

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