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Journal articles
Improving anaerobic conversion of pulp mill secondary sludge to biogas by pretreatment, TAPPI JOURNAL June 2010
Improving anaerobic conversion of pulp mill secondary sludge to biogas by pretreatment, TAPPI JOURNAL June 2010
Journal articles
Comparison of ultraviolet inkjet printing on different synth
Comparison of ultraviolet inkjet printing on different synthetic fibrous papers, TAPPI JOURNAL May 2010
Journal articles
A new method for quantifying \ the blocking of coated paperboard, TAPPI JOURNAL May 2010
A new method for quantifying \ the blocking of coated paperboard, TAPPI JOURNAL May 2010
Journal articles
Changes with aging in the surface hydrophobicity of coated paper, TAPPI JOURNAL May 2010
Changes with aging in the surface hydrophobicity of coated paper, TAPPI JOURNAL May 2010
Journal articles
Effect of ink and paperboard characteristics on flexographic
Effect of ink and paperboard characteristics on flexographic print quality based on print density, TAPPI JOURNAL September 2011
Journal articles
Conference review: Recycling sessions at TAPPI PEERS, TAPPI JOURNAL February 2012
Conference review: Recycling sessions at TAPPI PEERS, TAPPI JOURNAL February 2012
Journal articles
Novel blend of biorenewable wet-end paper agents, TAPPI JOURNAL January 2012
Novel blend of biorenewable wet-end paper agents, TAPPI JOURNAL January 2012
Journal articles
Paper machine white water deaeration and two-stage flotation with channel flow, TAPPI JOURNAL July 2012
Paper machine white water deaeration and two-stage flotation with channel flow, TAPPI JOURNAL July 2012
Journal articles
Microstructure simulation of early paper forming using immersed boundary methods, TAPPI JOURNAL November 2011
Microstructure simulation of early paper forming using immersed boundary methods, TAPPI JOURNAL November 2011
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.