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Root cause analysis of cationic polymer additive efficiency decline in virgin and recycle containerboard mills, TAPPI Journal January 2020

ABSTRACT: It is well known that retention, drainage and strength polymers struggle to perform (if at all) in virgin containerboard mills. In-depth studies have been undertaken in this area for more than seven years, investigating the issue from all directions. A key finding of this work is that soluble lignin is detrimental to chemical efficiency. A strong correlation exists between decreased chemical efficiency and high soluble lignin. Both recycled systems and virgin systems have been studied, and this correlation holds true regardless of furnish. The primary area of concern is virgin container-board, because these mills tend to have the highest lignin levels. Some highly closed recycled mills can also build elevated lignin levels that can negatively affect chemical efficiency.

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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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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)

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Good gravy, davy, TAPPI JOURNAL, February 2000, Vol. 83(2)

Good gravy, davy, TAPPI JOURNAL, February 2000, Vol. 83(2)

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Green bay packaging's continuing journey of excellence - rebuild of pm 2, TAPPI JOURNAL, February 2000, Vol. 83(2)

Green bay packaging's continuing journey of excellence - rebuild of pm 2, TAPPI JOURNAL, February 2000, Vol. 83(2)

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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)

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Innovations in forming earn brian attwood TAPPI's 2000 Gunnar Nicholson Gold Medal, TAPPI JOURNAL, February 2000, Vol. 83(2)

Innovations in forming earn brian attwood TAPPI's 2000 Gunnar Nicholson Gold Medal, TAPPI JOURNAL, February 2000, Vol. 83(2)

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Predicting the press dewatering of heavyweight grades, TAPPI JOURNAL, February 2000, Vol. 83(2)

Predicting the press dewatering of heavyweight grades, TAPPI JOURNAL, February 2000, Vol. 83(2)