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Magazine articles
Open Access
Using climate change policies for u.s. pulp and paper industry efficiency, TAPPI JOURNAL, July 2000, Vol. 83(7)

Using climate change policies for u.s. pulp and paper industry efficiency, TAPPI JOURNAL, July 2000, Vol. 83(7)

Magazine articles
Open Access
What's new with tappi test methods?, TAPPI JOURNAL, September 2000, Vol. 83(9)

What's new with tappi test methods?, TAPPI JOURNAL, September 2000, Vol. 83(9)

Magazine articles
Open Access
Advertising trends for publishers and magazines with the most ad pages, TAPPI JOURNAL, October 2000, Vol. 83(10)

Advertising trends for publishers and magazines with the most ad pages, TAPPI JOURNAL, October 2000, Vol. 83(10)

Magazine articles
Open Access
Challenges and opportunities for china's paper industry, TAPPI JOURNAL, October 2000, Vol. 83(10)

Challenges and opportunities for china's paper industry, TAPPI JOURNAL, October 2000, Vol. 83(10)

Magazine articles
Open Access
People development in research and technology organizations, TAPPI JOURNAL, October 2000, Vol. 83(10)

People development in research and technology organizations, TAPPI JOURNAL, October 2000, Vol. 83(10)

Magazine articles
Subscription Access
Toronto spotlight shines on tappi conference polymers, laminations, and coatings, TAPPI JOURNAL, January 1998, Vol. 81(1)

Toronto spotlight shines on tappi conference polymers, laminations, and coatings, TAPPI JOURNAL, January 1998, Vol. 81(1)

Magazine articles
Researcher's toolbox, International Nonwovens Journal, December 2001, Vol. 10(4)

Researcher's toolbox, International Nonwovens Journal, December 2001, Vol. 10(4)

Magazine articles
Director's corner, International Nonwovens Journal, September 2001, Vol. 10(3)

Director's corner, International Nonwovens Journal, September 2001, Vol. 10(3)

Journal articles
Magazine articles
Open Access
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
Over the Wire, Paper360º November/December 2019

Over the Wire, Paper360º November/December 2019

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