Abstract
Calorific value (CV) reflects the ability of material flow and energy conversion of plants, which is the key indices of combustion properties for utilization and development of energy plants. However, the commonly used method for CV determination of solid fuels is bomb calorimetry in the laboratory using powder samples, which hinders the capability of rapid and non-destructive prediction for a large-scale samples in a natural environment. Visible and near infrared spectroscopy (Vis-NIR) has been widely proposed as a replace for laboratory determination in properties prediction. However, chemometrics are essential for spectral analysis. Various chemometrics including competitive adaptive reweighted sample (CARS), lifting wavelet transform (LWT), successive projections algorithm (SPA), and convolutional neural networks (CNNs) optimized by whale optimization algorithm (WOA) were employed to optimize models. Additionally, canopy spectra were measured in the field instead of powder samples’ spectra collecting from laboratory. The results demonstrated that CARS-WOA-CNN was the best to predict CV and ash content (AC) with R2 of 0.858 and 0.751, respectively. Compared to raw full spectra, spectral dimension was reduced from 2048 to 93 and 22 for CV and AC, respectively. Overall, this study provided a meaningful strategy for harvest planning and assessing value of biomass in the field.
Published Version
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