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Near infrared technique as a tool for the rapid assessment of waste wood quality for energy applications

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  • Mancini, M.
  • Rinnan, Å.

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.

Suggested Citation

  • Mancini, M. & Rinnan, Å., 2021. "Near infrared technique as a tool for the rapid assessment of waste wood quality for energy applications," Renewable Energy, Elsevier, vol. 177(C), pages 113-123.
    • Handle: RePEc:eee:renene:v:177:y:2021:i:c:p:113-123
    DOI: 10.1016/j.renene.2021.05.137
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    References listed on IDEAS

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    1. Gillespie, Gary D. & Everard, Colm D. & McDonnell, Kevin P., 2015. "Prediction of biomass pellet quality indices using near infrared spectroscopy," Energy, Elsevier, vol. 80(C), pages 582-588.
    2. Millward-Hopkins, Joel & Purnell, Phil, 2019. "Circulating blame in the circular economy: The case of wood-waste biofuels and coal ash," Energy Policy, Elsevier, vol. 129(C), pages 168-172.
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    4. Sosa, Amanda & Acuna, Mauricio & McDonnell, Kevin & Devlin, Ger, 2015. "Controlling moisture content and truck configurations to model and optimise biomass supply chain logistics in Ireland," Applied Energy, Elsevier, vol. 137(C), pages 338-351.
    5. Chadwick, Dara T. & McDonnell, Kevin P. & Brennan, Liam P. & Fagan, Colette C. & Everard, Colm D., 2014. "Evaluation of infrared techniques for the assessment of biomass and biofuel quality parameters and conversion technology processes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 672-681.
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    1. Lima, Michael Douglas Roque & Ramalho, Fernanda Maria Guedes & Trugilho, Paulo Fernando & Bufalino, Lina & Dias Júnior, Ananias Francisco & Protásio, Thiago de Paula & Hein, Paulo Ricardo Gherardi, 2022. "Classifying waste wood from Amazonian species by near-infrared spectroscopy (NIRS) to improve charcoal production," Renewable Energy, Elsevier, vol. 193(C), pages 584-594.

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