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On the modeling of tensile index from larger data sets
Chalmers University of Technology, Sweden.
RISE - Research Institutes of Sweden, Bioeconomy, PFI.ORCID iD: 0000-0001-5522-0009
QualTech, Sweden.
2019 (English)In: Nordic Pulp & Paper Research Journal, ISSN 0283-2631, E-ISSN 2000-0669Article in journal (Refereed) Epub ahead of print
Abstract [en]

The objective of this study is to analyze and foresee potential outliers in pulp and handsheet properties for larger data sets. The method is divided into two parts comprising a generalized Extreme Studentized Deviate (ESD) procedure for laboratory data followed by an analysis of the findings using a multivariable model based on internal variables (i. e. process variables like consistency and fiber residence time inside the refiner) as predictors. The process data used in this has been obtained from CD-82 refiners and from a laboratory test program perspective, the test series were extensive. In the procedure more than 290 samples were analyzed to get a stable outlier detection. Note, this set was obtained from pulp at one specific operating condition. When comparing such "secured data sets" with process data it is shown that an extended procedure must be performed to get data sets which cover different operating points. Here 100 pulp samples at different process conditions were analyzed. It is shown that only about 60 percent of all tensile index measurements were accepted in the procedure which indicates the need to oversample when performing extensive trials to get reliable pulp and handsheet properties in TMP and CTMP processes.

Place, publisher, year, edition, pages
De Gruyter Open Ltd , 2019.
Keywords [en]
CTMP, energy efficiency, fiber residence time, modeling, pulp and handsheet properties, pulp consistency, temperature profile, tensile index, TMP, Models, Pulp refining, Software testing, Statistics, Handsheet properties, Residence time, Temperature profiles, Thermomechanical pulping process
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-39646DOI: 10.1515/npprj-2018-0019Scopus ID: 2-s2.0-85068744152OAI: oai:DiVA.org:ri-39646DiVA, id: diva2:1341049
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-08-07Bibliographically approved

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Johansson, Lars

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