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Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis

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  • Pitak, Lakkana
  • Sirisomboon, Panmanas
  • Saengprachatanarug, Khwantri
  • Wongpichet, Seree
  • Posom, Jetsada

Abstract

The use of biomass pellets as a renewable energy source is increasing, leading to the need for rapid assessment of biofuel pellet quality for production monitoring. The purpose of this work was to use line-scan near-infrared (NIR) hyperspectral image technology coupled with chemometric tools to assess the elemental components of biomass pellets. The parameters influencing model performance were investigated, i.e. wavelength and spectral pretreatment technique. Either full wavelength or partial wavelength selected using interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA) were investigated. Either raw spectra or pretreated spectra were used for model development. The models were developed using partial least squares regression (PLSR). The most effective model for the prediction of carbon (C), hydrogen (H), and nitrogen (N) content was developed using iGA wavelength selection and standard normal variate (SNV) spectral pretreatment and provided the highest accuracy with a coefficient of determination of prediction set (r2p) and standard error of prediction (SEP) of 0.83 and 1.33%; 0.84 and 0.17%; and 0.90 and 0.098%, respectively. The model could be used for quality assurance. The S content model was poor and not recommended. The relationship between pellet chemical parameters and reflectance characteristics could be used for predicting C, H, and N of biomass pellets.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:220:y:2021:i:c:s036054422032805x
    DOI: 10.1016/j.energy.2020.119698
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    1. Chen, Chao & Liang, Rui & Ge, Yadong & Li, Jian & Yan, Beibei & Cheng, Zhanjun & Tao, Junyu & Wang, Zhenyu & Li, Meng & Chen, Guanyi, 2022. "Fast characterization of biomass pyrolysis oil via combination of ATR-FTIR and machine learning models," Renewable Energy, Elsevier, vol. 194(C), pages 220-231.

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