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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.
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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.
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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.
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Editorial: Professional Networking in the Nonwovens and Technical Textiles Sector
Editorial: Professional Networking in the Nonwovens and Technical Textiles Sector, TAPPI Journal, May 2017
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Assessing variation in package modeling, TAPPI Journal April 2021
ABSTRACT: Predictions from empirical models are affected by variability in the input parameters for the data set used to build the models. For corrugated boxes, the difference between actual and modeled compression strength creates a real cost associated with box production, often resulting in boxes that may need to be over-designed to compensate for a lack of model precision. No work to date has attempted to assess the limitation in these compression estimates due to input parameter testing variability. In this paper we approach that problem, initially for the McKee equation and then conceptually for other box models. For our industry to do a better job at meeting the needs of our corrugated packaging customers, we need to reduce the variation in the tests we all rely on, particularly for evaluating material strength (edge crush test [ECT]) and package compression performance (box compression test [BCT]).
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Use of fines-enriched chemical pulp to increase CTMP strength, TAPPI Journal April 2021
ABSTRACT: In this study, fines-enriched pulp (FE-pulp)—the fine fraction of highly-refined kraft pulp—was benchmarked against highly-refined kraft pulp (HRK-pulp) as a strength agent in eucalyptus chemithermomechanical pulp (CTMP). Both the FE-pulp and the HRK-pulp were produced from unbleached softwood kraft pulp, and equal amounts of those strength agents were added to the original CTMP, as well as to washed CTMP, where most of the fines had been removed. The effects of the added strength agents were evaluated with laboratory handsheets.The FE-pulp proved to be twice as effective as HRK-pulp. Both HRK-pulp and FE-pulp increased the strength of the CTMP handsheets. The bulk of the handsheets decreased, however, as well as the drainability. The addition of 5% FE-pulp resulted in the same strength increase as an addition of 10% HRK-pulp, as well as the same decrease in bulk and CSF. For the handsheets of washed CTMP, the strengths were not measurable; the CTMP lost the sheet strength when the CTMP-fines content was reduced through washing. The reduced strength properties were compensated for by the addition of chemical pulp fines that proved to be an efficient strength agent. The addition of 5% FE-pulp restored the strength values, and at a higher bulk and higher drainability.
Journal articles
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Modeling and parameter optimization of the papermaking processes by using regression tree model and full factorial design, TAPPI Journal February 2021
ABSTRACT: One of the major challenges in the pulp and paper industry is taking advantage of the large amount of data generated through its processes in order to develop models for optimization purposes, mainly in the papermaking, where the current practice for solving optimization problems is the error-proofing method. First, the multiple linear regression technique is applied to find the variables that affect the output pressure controlling the gap of the paper sheet between the rod sizer and spooner sections, which is the main cause of paper breaks. As a measure to determine the predictive capacity of the adjusted model, the coefficient of determination (R2) and s values for the output pressure were considered, while the variance inflation factor was used to identify and eliminate the collinearity problem. Considering the same amount of data available by using machine learning, the regression tree was the best model based on the root mean square error (RSME) and R2. To find the optimal operating conditions using the regression tree model as source of output pressure measurement, a full factorial design was developed. Using an alpha level of 5%, findings show that linear regression and the regression tree model found only four independent variables as significant; thus, the regression tree model demonstrated a clear advantage over the linear regression model alone by improving operating conditions and demonstrating less variability in output pressure. Furthermore, in the present work, it was demonstrated that the adjusted models with good predictive capacity can be used to design noninvasive experiments and obtain.
Journal articles
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Editorial: Change, reality, and vision in the pulp and paper industry, TAPPI Journal February 2021
ABSTRACT: Change is inevitable and is a constant in our lives. Change is especially evident in this era of whirlwind, revolutionary break-throughs in communication and control technology. If nothing else, the past year has certainly made this clear.
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Editorial: Agility and adaptation in a dynamic business world, TAPPI Journal January 2021
ABSTRACT: Being agile and adaptive in this current business world is both necessary and beneficial. Throughout last year, we all continued to both directly and indirectly experience the dynamic world where we live. As we wll know, in early 2020 the pandemic gained momentum and has fundamentally altered our daily lives, both personally in how we live and professionally in how we work.
Journal articles
Magazine articles
Spraying starch on the Fourdrinier— An option between wet end starch and the size press, TAPPI Journal January 2021
ABSTRACT: Technology to apply suspensions of starch grains to the wet surface of paper, during the dewatering process, is reviewed. Though the technology is not new, it continues to attract the attention of papermakers as a means to increase bonding strength. Starch grains that are sprayed onto the wet-web of paper can be retained at levels exceeding what can be effectively added to the fiber suspension at the wet end. Unlike adding a starch solution at a size press, no additional drying capacity is required on the paper machine. To be effective, the starch needs to be able to swell and develop bonding during the paper drying process. Paperboard applications with recycled fibers appear to be a good fit. There is potential to increase bonding by processes that favor fuller gelatinization of the starch grains by the time the paper becomes dry.