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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.
Journal articles
Magazine articles
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.
Journal articles
Magazine articles
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.
Journal articles
Magazine articles
Polyvinyl alcohol as foaming agent in foam formed paper, TAPPI JOURNAL August 2019
ABSTRACT: The use of polyvinyl alcohol (PVOH or PVA) as a foaming agent in foam formed paper was investigated. Polyvinyl alcohol is a linear, nonionic water-soluble polymer. It has hydrophobic and hydrophilic parts that give it a surface-active character. PVOH is mainly characterized by degree of hydrolysis and molar mass. Degree of hydrolysis is given as mol-% hydroxyl groups on the polymer. Molar mass is measured indirectly by measuring the viscosity of a 4% PVOH solution. The results show that the degree of hydrolysis of PVOH had a strong effect on the foamability of PVOH. Foamability decreased strongly when the degree of hydrolysis increased from 88 to 98 mol-%. The effect of molar mass on foamability was weaker. We saw an increase in foam stability and bubble size with increasing molar mass, but we did not see any effect on maximum air content. PVOH dosage needed to reach >70% air content (F) varied from 2 g/l up to 10.5 g/l, and the lowest addition levels of PVOH needed were achieved with a low molar mass PVOH with a low degree of hydrolysis. The best strength properties were achieved when using fully hydrolyzed PVOH as the foaming agent. Strength properties (both in- and out-of-plane) of samples made using PVOH were better than those made using an anionic foaming agent (sodium dodecyl sulfate, SDS). By adding PVOH binder fibers to the pulp, we were able to further enhance the strength properties of paper and board.
Journal articles
Magazine articles
Factors affecting the free shrinkage of handsheets: apparent density, fines content, water retention value, and grammage, TAPPI JOURNAL June 2018
Factors affecting the free shrinkage of handsheets: apparent density, fines content, water retention value, and grammage, TAPPI JOURNAL June 2018
Journal articles
Magazine articles
Creasing severity and reverse-side cracking, TAPPI Journal April 2020
ABSTRACT: Crease cracking can be detrimental to the functionality and appearance of paperboard-based packaging. The effect of creasing severity on the degree of reverse-side crease cracking (bead-side of the crease) of paperboard was investigated. Samples were creased with a range of rule and channel geometries, and the cracking degree was quantified as the percent of cracked length relative to the total length of the crease. The cracking degree was typically below 5% at low crease penetration depths, but was exponentially higher beyond a critical penetration depth. A rule and channel combination with a wider clearance shifted the critical depth to larger values. The creasing severity parameter, termed the creasing draw, converged the cracking degree data from different rule and channel combinations to a single curve. The creasing draw was derived from the same analytical expres-sions as the transverse shear strain and quantifies the length of paper that is drawn into the channel during creasing. The critical draw is defined as the draw at which cracking becomes greater than 5%, which corresponds with the point at which cracking becomes exponential. The critical draw is a material/system parameter that defines the level below which cracking is minimal.
Journal articles
Biomass gasification for green ammonia production in kraft mills, TAPPI Journal September 2026
ABSTRACT: As the global energy sector transitions toward net-zero emissions, kraft pulp mills present a compelling opportunity to produce green fuels and achieve net-negative emissions by capturing biogenic carbon, helping offset hard-to-abate sectors. This study explores the techno-economics of integrating biomass gasification for green ammonia production within kraft mills, using KraftSIM modeling to evaluate ammonia production, steam balance, emissions, and utility impacts. The proposed design enables energy and water system integration with the existing mill and allows for a gradual scale-up without disrupting the chemistry and operation of the recovery cycle. Across five scenarios representing different biomass gasification rates ranging from 100 to 1000 bone-dry metric tons per day (BDMT/d), the process yields ammonia at approximately 0.51 metric tons per bone-dry metric ton (t/ BDMT), while generating significant low- and medium-pressure steam and hot water that offset mill utility demands. The incineration of pressure swing adsorption tail gas and ammonia plant purge gas in the power boiler reduces biomass combustion and enhances power boiler efficiency. Economic analysis suggests a net revenue potential of approximately CA$380/BDMT, which could increase with the implementation of biogenic carbon pricing. Overall profitability is strongly influenced by biomass procurement costs, electricity prices, and prevailing green fuel and chemical market premium. This work outlines a practical, near-term decarbonization pathway for kraft mills by coupling green fuel production with potential opportunities for carbon removal.
Journal articles
Magazine articles
Colloid chemical aspects of paper formation in the presence of nanofibrillated cellulose and cationic starch, TAPPI Journal September 2024
ABSTRACT: A series of experimental tests were carried out to examine colloidal-scale consequences of optionally treating nanofibrillated cellulose (NFC) with cationic starches of different charge density and dosage (0.5% or 2.0% by weight), adding that material to a furnish prepared from 100% recycled copy paper, and then subjecting the mixture to very different levels of hydrodynamic shear. Tests included optical microscopy, sediment volume tests, sediment velocity tests, and “percent fines” assessment by means of a fiber quality analyzer (FQA). In addition, the zeta potential and charge demand of the studied materials were evaluated. Optical imaging revealed that cationic starch treatment of the NFC tended to agglomerate it into multiparticle clusters, which sometimes could be mostly redispersed by hydrodynamic shear. Subsequent addition of the starch-treated NFC to the default furnish resulted in much of the colloidal material becoming attached to fibers. Subsequent shearing of the mixtures was at least partly effective in separating the clusters of NFC from the fiber surface, resulting in essentially a two-component mixture. Multiparticle NFC clusters coexisted with the fiber suspension, sometimes attached and sometimes not, depending on the details of treatments. Sediment volume tests showed that systems containing cationic starch-treated NFC tended to have a higher density after settling in comparison to untreated NFC; these findings are consistent with the cationic starch acting as a stabilizer on the solid surfaces, allowing them to slide past each other during the settling process. Application of intense hydrodynamic shear tended to result in denser sediment. Results of tests with the sediment velocity messurement and the FQA percent fines assessment did not correlate well with changes in test conditions considered in this study.
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.