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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
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
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.
Journal articles
Magazine articles
Review of coating cracking and barrier integrity on paperboard substrates, TAPPI JournalDecember 2024
Authors: Joel C. Panek and Peter W. Hart | TAPPI J. 21(11): 589(2022) - ABSTRACT: Barrier packaging formats are major growth areas for the pulp and paper industry. It is technically challenging to maintain barrier properties during converting and end-use applications. Improved manufacturing capabilities and coating formulation knowledge will help maintain barrier integrity and enable growth of barrier products in challenging applications. These improvements will accelerate product development and commercialization, and allow faster response to product performance issues such as cracking. The literature on coating cracking provides knowledge mostly on the effects of coating formulations and to a lesser extent on substrate effects. Despite a large number of publications dedicated to coating failures, the approach to improve coating cracking remains empirical, and the transferability between studies and to real life applications has not been well established. Model development that successfully predicts commercial performance is in its infancy. However, some of these simplified models do a fairly good job predicting experimental data. The current work reviews the state of understanding as regards coating and barrier cracking and highlights the need for more research on cracking and barrier integrity.
Journal articles
Shop talk: troubleshooting moisture profile problems in the cross-machine direction, TAPPI JOURNAL, July 2000, Vol. 83(7)
Shop talk: troubleshooting moisture profile problems in the cross-machine direction, TAPPI JOURNAL, July 2000, Vol. 83(7)
Journal articles
Adsorption of anionic dissolved and colloidal substances onto calcium carbonate fillers, TAPPI JOURNAL, July 2000, Vol. 83(7)
Adsorption of anionic dissolved and colloidal substances onto calcium carbonate fillers, TAPPI JOURNAL, July 2000, Vol. 83(7)
Journal articles
Impact of the cluster rule on the cost competitiveness of the u.s. papermaking industry in the global market, TAPPI JOURNAL, September 2000, Vol. 83(9)
Impact of the cluster rule on the cost competitiveness of the u.s. papermaking industry in the global market, TAPPI JOURNAL, September 2000, Vol. 83(9)
Journal articles
An inside view on impulse drying phenomena by modelling, TAPPI JOURNAL, October 2000, Vol. 83(10)
An inside view on impulse drying phenomena by modelling, TAPPI JOURNAL, October 2000, Vol. 83(10)
Journal articles
Influence of coating rheology on final coating properties, TAPPI JOURNAL, February 2000, Vol. 83(2)
Influence of coating rheology on final coating properties, TAPPI JOURNAL, February 2000, Vol. 83(2)
Journal articles
Characterization of tensile fracture properties of paper, TAPPI JOURNAL, February 2000, Vol. 83(2)
Characterization of tensile fracture properties of paper, TAPPI JOURNAL, February 2000, Vol. 83(2)