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Evaluation of lower heating value and elemental composition of bamboo using near infrared spectroscopy

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  • Posom, Jetsada
  • Sirisomboon, Panmanas

Abstract

This study was to optimize models using near infrared spectroscopy for evaluation of lower heating value, carbon, hydrogen, nitrogen, sulfur and oxygen content of bamboo. Partial least squares regression was performed using 80 samples of bamboo where 64 samples was randomly assigned for calibration and 16 samples for validation. The result was that lower heating value and element composition did not depend on circumference size. The models showed the coefficient of determination of validation set and ratio of standard error of prediction to standard deviation of reference value of 0.934 and 3.96 for lower heating value, 0.803 and 2.31 for carbon; 0.856 and 2.65 for hydrogen; 0.973 and 6.6 for nitrogen; 0.785 and 2.19 for sulfur and 0.522 and 1.46 for oxygen, respectively. Models for lower heating value, nitrogen and hydrogen content prediction provided the highest performance, which could be used for most application. The carbon and sulfur models were fair and oxygen model was poor. The result could be a guide for applying in bamboo trading as biomass, in design and operation control of energy conversion and in predicting flue gas flow rate, air requirement, and flue gas compositions in combustion, gasification or pyrolysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:121:y:2017:i:c:p:147-158
    DOI: 10.1016/j.energy.2017.01.020
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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. Xue, Junjie & Yang, Zengling & Han, Lujia & Liu, Yuchen & Liu, Yao & Zhou, Chengcheng, 2015. "On-line measurement of proximates and lignocellulose components of corn stover using NIRS," Applied Energy, Elsevier, vol. 137(C), pages 18-25.
    4. Chadwick, Dara T. & McDonnell, Kevin P. & Brennan, Liam P. & Fagan, Colette C. & Everard, Colm D., 2014. "Evaluation of infrared techniques for the assessment of biomass and biofuel quality parameters and conversion technology processes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 672-681.
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    Cited by:

    1. Pitak, Lakkana & Sirisomboon, Panmanas & Saengprachatanarug, Khwantri & Wongpichet, Seree & Posom, Jetsada, 2021. "Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis," Energy, Elsevier, vol. 220(C).
    2. Bijendra Shrestha & Jetsada Posom & Panmanas Sirisomboon & Bim Prasad Shrestha, 2023. "Comprehensive Assessment of Biomass Properties for Energy Usage Using Near-Infrared Spectroscopy and Spectral Multi-Preprocessing Techniques," Energies, MDPI, vol. 16(14), pages 1-26, July.
    3. Sirisomboon, Panmanas & Funke, Axel & Posom, Jetsada, 2020. "Improvement of proximate data and calorific value assessment of bamboo through near infrared wood chips acquisition," Renewable Energy, Elsevier, vol. 147(P1), pages 1921-1931.
    4. Łukasz Sobol & Dominika Sabat & Arkadiusz Dyjakon, 2023. "Assessment of Bark Properties from Various Tree Species in Terms of Its Hydrophobicity and Energy Suitability," Energies, MDPI, vol. 16(18), pages 1-21, September.

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