TAPPI Journal Editorial Board Selects Best Research Paper

The editorial board of TAPPI Journal has selected "Leveraging mill-wide big data sets for process and quality improvement in paperboard production," by Jianzhong Fu and Peter W. Hart as TAPPI Journal’s Best Research Paper for 2016. The paper appeared in the May 2016 issue and was one of eight nominated for the award. The paper and its authors will be honored at the Awards Gala Dinner on April 25 during PaperCon, in Minneapolis, MN.

According to Hart, the authors chose to research this topic because "traditional troubleshooting techniques were not getting to the root cause of indent formation on the paper."  He also notes that a significant amount of effort was required to clean up and analyze the data. Once that was done, group meetings were held with operators and industry experts to successfully eliminate events that correlated with problems but were not direct contributors to the problem. Hart said that "it’s easy to find correlation of variables with limited causation."

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 determine root causes of this negative and increasingly frequent situation. 

Nearly 6,000 operating variables were selected for a deep dive, multi-year analysis after reviewing mill-wide process logs and 60,000+ 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 pulping, 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, and several projects were initiated.

Papermaking, like other manufacturing, is facing an unprecedented explosion in data collection and availability. Big data analytics can develop predictive models and find critical insights that are essential to improve operational performance. 

Fu is senior technical engineer for MWV (now WestRock) in Covington, VA, USA. Hart is director — Fiber Science for MWV (now WestRock) in Atlanta, GA, USA. 

TAPPI Journal is an internationally recognized peer-reviewed technical journal publishing top-quality basic and applied research. If you, your colleagues, and/or students are conducting research in pulping, papermaking, tissue, packaging, nonwovens, nanotechnology, biorefining or biofuels, you can submit your papers for publication in TAPPI Journal at no charge.