期刊
PROCESSES
卷 9, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/pr9020316
关键词
fuel ratio; proximate data; NIR hyperspectral imaging; wavelength selection; biomass pellet; in-line measurement
资金
- Research and Academic Services, Khon Kaen University, Thailand
- Research and Graduate Studies Khon Kaen University, Thailand
- Research Grant for New Scholar of the Thailand Research Fund (TRF), Thailand [MRG6280078]
This study utilized NIR hyperspectral images to predict the properties of commercial biomass pellets, finding that using different spectral preprocessing techniques and wavelengths can improve the prediction accuracy.
Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R-2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets' properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.
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