Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment.

Due to their characteristics, Caragana Korshinskii bars are prone to be crushed by kneaded crushed material, and the kneaded crushed material is prone to clogging the sieve, thus affecting the crushing effect. In this paper, we use the Caragana Korshinskii special hammer crusher to carry out the Caragana Korshinskii cutting and crushing test to study the influence of Caragana Korshinskii diameter, cutting length, and moisture content on the crushing effect of Caragana Korshinskii, and obtain the best process parameters through the response surface method. The results were as follows: When the diameter of Caragana Korshinskii strips was in the range of 6–9 mm, the length of the cut section was in the range of 4–8 cm, and the moisture content of Caragana Korshinskii strips was in the range of 10–19%, the Caragana Korshinskii strips crushing effect was the best, and the kneading situation was the lowest. The optimal Caragana Korshinskii crushing effect was achieved when the diameter of the Caragana Korshinskiibar was 6.5 mm, the length of the cut section was 8.4 cm, the moisture content was 19%, the sieve rate of 2 mm was 46.15%, the sieve rate of 4 mm was 66.32%, and the length of the kneaded wire was 3.3 cm.