Aiming at the lack of a precise discrete element model in the simulation analysis of corn straw crushing, this paper established bond models with different structure and particle models with different properties of corn straw based on Hertz-Mindlin with bonding, and verified the discrete element model by combining physical test and simulation optimization design methods. Firstly, this study generates the bonded particle models based on Hertz-Mindlin with bonding for epidermis-epidermis, inner flesh-internal flesh, and epidermis-internal flesh, respectively. The bonding parameters of the model are calibrated with the help of a bending damage test, bending damage simulation test, and actual test with 2 mm/min speed. It shows that the maximum destructive force errors of the epidermis and inner flesh are 2.4% and 1.6%, respectively. On this basis, a discrete element model of corn straw is established by combining the calibrated parameters of the bonding parameters of epidermis—epidermis, inner flesh—inner flesh, and epidermis—inner flesh bond. The bending failure test shows that the mechanical properties of corn straw are similar between the simulated test and the actual test, and the maximum destructive power is 288 N and 292 N, respectively. The relative error is 1.36%. The feasibility of the discrete element model for simulation analysis is verified, which shows that the established discrete element model can be applied to the simulation analysis of corn straw cutting and crushing process.

Moisture content is an important parameter for estimating the quality of pellet feed, which is vital in nutrition, storage, and taste. The ranges of moisture content serve as an index for factors such as safe storage and nutrition stability. A rapid and non-destructive model for the measurement of moisture content in pellet feed was developed. To achieve this, 144 samples of Caragana korshinskii pellet feed from various regions in Inner Mongolia Autonomous Region underwent separate moisture content control, measurement using standard methods, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 935.5–2539 nm. The Monte Carlo cross validation (MCCV) was used to eliminate abnormal sample data from the spectral data for better model accuracy, and a global model of moisture content was built by using partial least squares regression (PLSR) with seven preprocessing techniques and two spectral feature extraction techniques. The results showed that the regression model developed by PLSR based on second derivative (SD) and competitive adaptive reweighted sampling (CARS) resulted in better performance for moisture content. The model showed predictive abilities for moisture content with a coefficient of determination of 0.9075 and a root mean square error (RMSE) of 0.4828 for the training set; and a coefficient of determination of 0.907 and a root mean square error (RMSE) of 0.5267 for the test set; and a relative prediction error of 3.3 and the standard error of 0.307.

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.

In order to perform quantitative analysis and predict the wear of the forming channel, this study makes biomass pellets as experimental samples. The surface morphology was scanned by a scanning electron microscope (SEM), and the surface morphology data and images were collected by a roughness instrument. Then, we collected the data of arithmetical average deviation Ra, density $\rho$, as well as hardness HD, and further calculated the fractal dimension D, which help to study the influencing factors of fractal dimension on the circular surface morphology of biomass pellet. The results show that, the density $\rho$ and hardness HD of biomass pellets both decreased and with the increase in diameter d, the arithmetical average deviation Ra increased with the diameter d, the quality of the pellet is reduced, meanwhile, the fractal dimension D also shows a downward trend. Using the value, trend of fractal dimension D to analyze the quality of biomass pellet can predict the wear status and life of forming channel in biomass briquette machine. It is concluded that, the fractal expression of surface morphology in biomass pellet relates to inner surface morphology of forming channel in biomass pelleting machine. Additionally, fractal dimension of the surface morphology of biomass pellet can be the basis of preliminary research for friction and wear detection and prediction of forming channel.