Binder-free torrefied biomass pellets: significance of torrefaction temperature and pelletization parameters by multivariate analysis

Torrefaction is a promising technology to improve fuel properties of biomass. However, torrefied char does not show densified energy content compared with coal and typically requires densification such as pelletization. Several key parameters including pelletization and torrefaction conditions were...

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Bibliographic Details
Published in:Biomass conversion and biorefinery 2022-06, Vol.12 (6), p.2175-2183
Main Authors: Saba, Akbar, Saha, Nepu, Williams, Keenan-Conrad, Coronella, Charles J., Reza, M. Toufiq
Format: Article
Language:English
Subjects:
Biomass
Biotechnology
Calorific value
Confidence intervals
Densification
Dependent variables
Drying
Energy
Independent variables
Mathematical models
Multivariate analysis
Original Article
Parameters
Pellets
Pressure dependence
Principal components analysis
Pyrolysis
Regression analysis
Renewable and Green Energy
Statistical analysis
Statistical significance
Variables
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Summary:Torrefaction is a promising technology to improve fuel properties of biomass. However, torrefied char does not show densified energy content compared with coal and typically requires densification such as pelletization. Several key parameters including pelletization and torrefaction conditions were investigated in order to determine the effects on energy density of torrefied char pellets. In this study, multivariate analyses were performed using three independent variables, torrefaction temperature, pelletization temperature, and pelletization pressure; and five dependent variables: mass density, durability, volume expansion, higher heating value (HHV), and energy density (ED). The variation and correlation of the dependent variables were evaluated using principal component analysis (PCA). Additionally, multivariate linear regression was performed to correlate independent variables and dependent variable responses using an adjusted sum of squares method and a two-sided, 90% confidence interval for the mean response; the model was fitted with statistically significant linear, quadratic, and interaction terms. The statistical analysis showed that all independent parameters are statistically significant for ED with the largest model variation from experimental data being 1.44%. Response curve of the regression analysis showed that mass density, HHV, and energy density model have the highest output predictability with higher R 2 values of 95.3%, 98.1%, and 74.1%, respectively.
ISSN:2190-6815
2190-6823
DOI:10.1007/s13399-020-00737-7