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Editorial: In praise of challenging papers

Editorial: In praise of challenging papers, and those who review them, TAPPI JOURNAL August 2015

Journal articles
Magazine articles
Open Access
Causality of manufacturing processes with significant time

Causality of manufacturing processes with significant time delays, TAPPI JOURNAL November 2015

Journal articles
Magazine articles
Open Access
Desilication of bamboo for pulp production, TAPPI JOURNAL N

Desilication of bamboo for pulp production, TAPPI JOURNAL November 2015

Journal articles
Magazine articles
Open Access
Editorial: Marc Foulger: All things papermaking, TAPPI JOUR

Editorial: Marc Foulger: All things papermaking, TAPPI JOURNAL November 2015

Journal articles
Magazine articles
Open Access
Guest Editorial: Paper and bioprocess engineering at SUNY-ES

Guest Editorial: Paper and bioprocess engineering at SUNY-ESF, TAPPI JOURNAL January 2012

Journal articles
Magazine articles
Open Access
Guest Editorial: J.Y. Zhu: Adapting and transforming the ind

Guest Editorial: J.Y. Zhu: Adapting and transforming the industry, TAPPI JOURNAL July 2012

Journal articles
Magazine articles
Open Access
Editorial: Professional Networking in the Nonwovens and Technical Textiles Sector

Editorial: Professional Networking in the Nonwovens and Technical Textiles Sector

Journal articles
Magazine articles
Open Access
Improved deinking and stickies removal

Improved deinking and stickies removal, TAPPI JOURNAL November 2017

Journal articles
Magazine articles
Open Access
Monitoring the free lime content in lime mud using zeta potential, TAPPI JOURANL April 2018

Monitoring the free lime content in lime mud using zeta potential, TAPPI JOURANL April 2018

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.

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