On the modeling of tensile index from larger data sets

Artikel i vetenskaplig tidskrift, 2019

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.