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Practical Strategies to Reduce Pulp and Paper Mill Energy Use, Paper360º July/August 2018
Practical Strategies to Reduce Pulp and Paper Mill Energy Use, Paper360º July/August 2018
Journal articles
Magazine articles
A Novel Synthetic Adsorbent for Pitch Control, Paper360º, Ja
A Novel Synthetic Adsorbent for Pitch Control, Paper360º, January/February 2018
Journal articles
Magazine articles
Are You in the ´Circle of Despair’?, Paper360º September/October
Are You in the ´Circle of Despair’?, Paper360º September/October
Journal articles
Magazine articles
The Paper360° TOP 100, Paper360º September/October
The Paper360° TOP 100, Paper360º September/October
Journal articles
Magazine articles
The Problem with Seal Water Systems, Paper360º September/October
The Problem with Seal Water Systems, Paper360º September/October
Journal articles
Magazine articles
UPM: The Forest Forerunner, Paper360º September/October
UPM: The Forest Forerunner, Paper360º September/October
Journal articles
Magazine articles
Hands-On Service Means Floods Are Gone, Paper360º July/August 2019
Hands-On Service Means Floods Are Gone, Paper360º July/August 2019
Journal articles
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Engineering: Meeting the Challenge, Paper360º July/August 2019
Engineering: Meeting the Challenge, Paper360º July/August 2019
Journal articles
Magazine articles
TAPPI Journal Summaries, Paper360 May/June 2019
TAPPI Journal Summaries, Paper360 May/June 2019
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.