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Establishing a model for the dry density of heartwood of Norway spruce by parameters industrially measurable on green logs
2005 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this study different models for the prediction of dry density from parameters measured on green wood was tried on different datasets. Data from old literature have been utilized to derive a multivariate PLS model and to compare the significance of different variables. The data was presented as average data for different stands from four different locations in Sweden. Validation was performed by applying the models on two different datasets: One small sample from southern Finland and the large data gathered by STFI and Skogforsk in the project “Skog Massa Papper”. The Finnish data was acquired by measuring properties of log stumps from CT-scanned images. Derived models were compared with an algebraic derived model and the density correction suggested by EN384. The multivariate model, using green density and position in the stem, can predict dry density of heartwood of Swedish Spruce with a R² of about 60% and a standard error of prediction of 27.4 kg/m³ on a sample disc as its best. This is slightly better compared to single variable models only utilizing the green density as variable. The correlation on the testset was 78% which is promising when considering that mill specific models should be made in case of industrial implementation which should also improve the models fit on a validation data. The model should also be tested with respect to the X-ray log scanners ability to measure the variables and the measurement error connected with the measurements.

Place, publisher, year, edition, pages
2005.
Keywords [en]
Technology, Wood, Norway spruce, Picea abies, density, PLS, multivariate, models, validation, prediction, X-ray, CT-scanning, logscanner, log grading, pre-grading, strength grading, heartwood, knots, annual ring width, image analysis
Keywords [sv]
Teknik
Identifiers
URN: urn:nbn:se:ltu:diva-56630ISRN: LTU-EX--05/207--SELocal ID: d63749ef-ed80-44ca-8ade-3114a56cca9cOAI: oai:DiVA.org:ltu-56630DiVA, id: diva2:1030017
Subject / course
Student thesis, at least 30 credits
Educational program
Wood Engineering, master's level
Examiners
Note
Validerat; 20101217 (root)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf