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

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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
    Center for Alternative Energy Research and Development, Khon Kaen University, Khon Kaen 40002, Thailand)

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.

Suggested Citation

  • Kanvisit Maraphum & Kantisa Phoomwarin & Nirattisak Khongthon & Jetsada Posom, 2025. "Eliminating Effect of Moisture Content in Prediction of Lower Heating Value and Ash Content in Sugarcane Leaves Biomass," Energies, MDPI, vol. 18(13), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3352-:d:1687869
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    References listed on IDEAS

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    1. Posom, Jetsada & Shrestha, Amrit & Saechua, Wanphut & Sirisomboon, Panmanas, 2016. "Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy," Energy, Elsevier, vol. 107(C), pages 464-472.
    2. Gillespie, Gary D. & Everard, Colm D. & McDonnell, Kevin P., 2015. "Prediction of biomass pellet quality indices using near infrared spectroscopy," Energy, Elsevier, vol. 80(C), pages 582-588.
    3. Posom, Jetsada & Sirisomboon, Panmanas, 2017. "Evaluation of lower heating value and elemental composition of bamboo using near infrared spectroscopy," Energy, Elsevier, vol. 121(C), pages 147-158.
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