Eliminating Effect of Moisture Content in Prediction of Lower Heating Value and Ash Content in Sugarcane Leaves Biomass

Kanvisit Maraphum, Kantisa Phoomwarin, Nirattisak Khongthon and Jetsada Posom ()
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Kanvisit Maraphum: Department of Agricultural Machinery, Faculty of Agriculture and Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand
Kantisa Phoomwarin: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Nirattisak Khongthon: Department of Agricultural Machinery, Faculty of Agriculture and Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand
Jetsada Posom: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand

Energies, 2025, vol. 18, issue 13, 1-17

Abstract: Accurate assessment of biomass fuel properties is essential for quality control and fair market pricing, particularly when dealing with variable moisture content (MC) in agricultural residues. This study investigates the use of near-infrared (NIR) spectroscopy to predict the lower heating value (LHV) and ash content of sugarcane leaf pellets while minimizing the interference caused by moisture variability. Sixty-two samples were scanned using an NIR spectrometer over three week-long storage periods to get different MCs with the same sample. Additionally, variable selection methods such as a genetic algorithm (GA) and moisture-related wavelength exclusion were explored. The optimal model for LHV prediction was developed using GA-PLS regression (Method II), provided a coefficient of determination (R 2 ) of 0.80, a root mean square error of calibration (RMSEc) of 595.80 J/g, and a ratio of performance to deviation (RPD) of 1.74, indicating fair predictive performance. The ash content model showed moderate accuracy, with a maximum R 2 of 0.61 and an RPD of 1.40. These findings suggest that the variables selected via GA in Method II were not relevant to MC; as Method II provided the best result, this indicates a low impact of MC, which may influence model construction in the future. Moreover, the findings also highlight the potential of NIR spectroscopy, combined with appropriate spectral preprocessing and wavelength optimization, as a rapid, non-destructive tool for evaluating biomass quality, enabling more precise control in bioenergy production and biomass trading.

Keywords: biomass; pellet; moisture content; time storage; variable selection; lower heating value; ash content (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: 2025
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