Near infrared technique as a tool for the rapid assessment of waste wood quality for energy applications
M. Mancini and
Å. Rinnan
Renewable Energy, 2021, vol. 177, issue C, 113-123
Abstract:
Considering the focus of the current European policy in promoting the reuse of waste products and increasing the share of renewable energies, waste wood is becoming an appealing resource rather than a product to dispose of. End-life waste wood products could be used for the production of panel board or as feedstock in combustion units. In this study, waste wood samples have been collected in a big panel board company, and have been analyzed by means of Near Infrared Spectroscopy. Principal Component Analysis has been used in order to investigate the variability of the material, and Partial-Least Squares regression models have been developed for the prediction of moisture content and net calorific value. The results indicate that both models could be used in quality control applications, and Near Infrared Spectroscopy can be considered as a tool for the rapid evaluation of waste wood parameters for energy applications. Considering the high correlation between the two parameters it is also possible to analyze only the moisture content and have indications about the net calorific value using a simple linear regression, with positive effects in terms of quality control and the reuse of the waste wood material.
Keywords: Moisture content; Net calorific value; Prediction; PCA; Variability; Recycling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014812100820X
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:177:y:2021:i:c:p:113-123
DOI: 10.1016/j.renene.2021.05.137
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().