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Etex Bègles Paper Mill Raises Paperboard Performance, Paper360º March/April 2019
Etex Bègles Paper Mill Raises Paperboard Performance, Paper360º March/April 2019
Journal articles
Magazine articles
TAPPI Journal Summaries, Paper360º March/April 2019
TAPPI Journal Summaries, Paper360º March/April 2019
Journal articles
Magazine articles
Is Your Preventive Maintenance Program Right for Your Plant?, Paper360º March/April 2019
Is Your Preventive Maintenance Program Right for Your Plant?, Paper360º March/April 2019
Journal articles
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Reinvention Brings Rewards for Cascades Sonoco, Paper360 May/June 2019
Reinvention Brings Rewards for Cascades Sonoco, Paper360 May/June 2019
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Magazine articles
2019 TAPPI/PIMA Awards: Saluting Our Industry Leaders, Paper360 May/June 2019
2019 TAPPI/PIMA Awards: Saluting Our Industry Leaders, Paper360 May/June 2019
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Facing the Market, Paper360 May/June 2019
Facing the Market, Paper360 May/June 2019
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The Shortest Route to Fiber Quality, Paper360 May/June 2019
The Shortest Route to Fiber Quality, Paper360 May/June 2019
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Wood Pulp: The New Cotton for the Garment Industry?, Paper360 May/June 2019
Wood Pulp: The New Cotton for the Garment Industry?, Paper360 May/June 2019
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Magazine articles
Meet the 2019 PPI Awards Winners, Paper360 May/June 2019
Meet the 2019 PPI Awards Winners, Paper360 May/June 2019
Magazine articles
Leveraging mill-wide big data sets for process and quality i
Authors: Jianzhong Fu and Peter W. Hart | ABSTRACT: The MWV mill in Covington, VA, USA, experienced a long term trend of increasing episodes of paper indents that resulted in significant quantities of internal rejects and production downtime. When traditional troubleshooting techniques failed to resolve the problem, big data analysis techniques were employed to help deter-mine root causes of this negative and increasingly frequent situation. Nearly 6000 operating variables were selected for a deep dive, multi-year analysis after reviewing mill-wide process logs and 60000+ PI tags (data points) collected from one of the major data historian systems at the MWV Covington mill. Nine billion data points were collected from November 2011 to August 2014. Strategies and methods were developed to format, clean, classify, and sort the various data sets to compensate for process lag time and to align timestamps, as well as to rank potential causes or indicators. GE Intelligent Platforms software was employed to develop decision trees for root cause analysis. Insights and possible correlations that were previously invisible or ignored were obtained across the mill, from pulp-ing, bleaching, and chemical recovery to the papermaking process. Several findings led the mill to revise selected process targets and to reconsider a step change in the drying process. These changes have exhibited significant impacts on the mill’s product quality, cost, and market performance. Mill-wide communications of the identified results helped transform the findings into executable actions. Several projects were initiated.