The Use of Multivariate Data Analysis (HCA and PCA) to Characterize Ashes from Biomass Combustion

Małgorzata Szczepanik, Joanna Szyszlak-Bargłowicz, Grzegorz Zając, Adam Koniuszy, Małgorzata Hawrot-Paw and Artur Wolak
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Małgorzata Szczepanik: Department of Applied Mathematics and Computer Science, Faculty of Production Engineering, University of Life Sciences in Lublin, Głeboka 28, 20-612 Lublin, Poland
Joanna Szyszlak-Bargłowicz: Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, Głeboka 28, 20-612 Lublin, Poland
Grzegorz Zając: Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, Głeboka 28, 20-612 Lublin, Poland
Adam Koniuszy: Department of Renewable Energy Engineering, West Pomeranian University of Technology, Papieża Pawła VI 1, 71-459 Szczecin, Poland
Małgorzata Hawrot-Paw: Department of Renewable Energy Engineering, West Pomeranian University of Technology, Papieża Pawła VI 1, 71-459 Szczecin, Poland
Artur Wolak: Department of Quality and Safety of Industrial Products, Institute of Quality and Product Management Sciences, Cracow University of Economics, Rakowicka 27, 31-510 Kraków, Poland

Energies, 2021, vol. 14, issue 21, 1-9

Abstract: The content of heavy metals Cd, Cr, Cu, Fe, Ni, Pb and Zn in ash samples from miscanthus, oak, pine, sunflower husk, wheat straw, and willow ashes burned at 500, 600, 700, 800, 900, and 1000 °C, respectively, was determined. The statistical analysis of the results was based on multivariate methods: hierarchical cluster analysis (HCA), and principal component analysis (PCA), which made it possible to classify the raw materials ashed at different temperatures into the most similar groups, and to study the structure of data variability. Using PCA, three principal components were extracted, which explain more than 88% of the variability of the studied elements. Therefore, it can be concluded that the application of multivariate statistical techniques to the analysis of the results of the study of heavy metal content allowed us to draw conclusions about the influence of biomass properties on its chemical characteristics during combustion.

Keywords: ash composition; biomass; multivariate data analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (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)

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