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A multivariate SIMCA index as discriminant in wood pellet quality assessment

Author

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  • Sgarbossa, Andrea
  • Costa, Corrado
  • Menesatti, Paolo
  • Antonucci, Francesca
  • Pallottino, Federico
  • Zanetti, Michela
  • Grigolato, Stefano
  • Cavalli, Raffaele

Abstract

The pellet market has experienced a continuous development and increase in recent years due to a number of positive properties of this enhanced biomass. However the supply chain has not been entirely able to follow the same trend, causing some issues, often related to the quality of traded products. These problems can be partially solved by ensuring a continuous and reliable flow of information regarding the quality parameters of wood pellets from the producers to the final users. The aim of this work is to define a metric index for quality parameters that can detect the certifiability of analyzed samples compared with those on the market. The model is built on measured quality parameters of certified and non-certified wood pellet samples taken from products on the market applying a multivariate class modelling methodology (soft independent modelling of class analogy, SIMCA). Results showed that the model can predict the general quality of some test samples and that its precision, already fairly high, can be constantly improved by adding new model samples. The output of the model is also the relative influence (modelling power) of each variable in the prediction of certifiability. The SIMCA model could be easily integrated and implemented on the most common digital platforms where users (private, laboratories, agencies, etc.) could test their samples and verify if the index of their pellet falls within the area defined by the model for certified samples.

Suggested Citation

  • Sgarbossa, Andrea & Costa, Corrado & Menesatti, Paolo & Antonucci, Francesca & Pallottino, Federico & Zanetti, Michela & Grigolato, Stefano & Cavalli, Raffaele, 2015. "A multivariate SIMCA index as discriminant in wood pellet quality assessment," Renewable Energy, Elsevier, vol. 76(C), pages 258-263.
    • Handle: RePEc:eee:renene:v:76:y:2015:i:c:p:258-263
    DOI: 10.1016/j.renene.2014.11.041
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    References listed on IDEAS

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    Cited by:

    1. Garcia, Dorival Pinheiro & Caraschi, José Cláudio & Ventorim, Gustavo & Vieira, Fábio Henrique Antunes & de Paula Protásio, Thiago, 2019. "Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA)," Renewable Energy, Elsevier, vol. 139(C), pages 796-805.
    2. Małgorzata Szczepanik & Joanna Szyszlak-Bargłowicz & Grzegorz Zając & Adam Koniuszy & Małgorzata Hawrot-Paw & Artur Wolak, 2021. "The Use of Multivariate Data Analysis (HCA and PCA) to Characterize Ashes from Biomass Combustion," Energies, MDPI, vol. 14(21), pages 1-9, October.
    3. Nunes, L.J.R. & Matias, J.C.O. & Catalão, J.P.S., 2016. "Wood pellets as a sustainable energy alternative in Portugal," Renewable Energy, Elsevier, vol. 85(C), pages 1011-1016.
    4. Duca, D. & Mancini, M. & Rossini, G. & Mengarelli, C. & Foppa Pedretti, E. & Toscano, G. & Pizzi, A., 2016. "Soft Independent Modelling of Class Analogy applied to infrared spectroscopy for rapid discrimination between hardwood and softwood," Energy, Elsevier, vol. 117(P1), pages 251-258.

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