Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning, TAPPI Journal November 2019



Application: This paper emphasizes the importance of harnessing the data available to papermakers for the purpose of predicting, in real time, their most critical quality parameters. Papermakers can use this information to understand the value-added opportunities that advanced analytics can bring their manufacturing operation.

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Author: Cydney Rechtin, Chitta Ranjain, Antohy Lewis, and Beth Ann Zarko
Creating adaptive predictions for packaging-critical quality
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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