A Principal Component Analysis in Switchgrass Chemical Composition

Mario Aboytes-Ojeda, Krystel K. Castillo-Villar, Tun-hsiang Yu, Christopher Boyer, Burton English, James Larson (), Lindsey M. Kline and Nicole Labbé
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Mario Aboytes-Ojeda: Mechanical Engineering Department, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA
Krystel K. Castillo-Villar: Mechanical Engineering Department, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA
Lindsey M. Kline: Center for Renewable Carbon, University of Tennessee, Knoxville, TN 37996, USA
Nicole Labbé: Center for Renewable Carbon, University of Tennessee, Knoxville, TN 37996, USA

Energies, 2016, vol. 9, issue 11, 1-12

Abstract: In recent years, bioenergy has become a promising renewable energy source that can potentially reduce the greenhouse emissions and generate economic growth in rural areas. Gaining understanding and controlling biomass chemical composition contributes to an efficient biofuel generation. This paper presents a principal component analysis (PCA) that shows the influence and relevance of selected controllable factors over the chemical composition of switchgrass and, therefore, in the generation of biofuels. The study introduces the following factors: (1) storage days; (2) particle size; (3) wrap type; and (4) weight of the bale. Results show that all the aforementioned factors have an influence in the chemical composition. The number of days that bales have been stored was the most significant factor regarding changes in chemical components due to its effect over principal components 1 and 2 (PC1 and PC2, approximately 80% of the total variance). The storage days are followed by the particle size, the weight of the bale and the type of wrap utilized to enclose the bale. An increment in the number of days (from 75–150 days to 225 days) in storage decreases the percentage of carbohydrates by ?1.03% while content of ash increases by 6.56%.

Keywords: lignocellulosic biomass; principal component analysis; statistical hypothesis; bioenergy; switchgrass (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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