In order to enhance the accuracy of discrete element numerical simulations in the processing of small-seed particles, it is essential to calibrate the parameters of seeds within the discrete element software. This study employs a series of physical tests to obtain the physical and contact parameters of red clover seeds. A discrete element model of red clover seeds is established. Plackett–Burman Design, steepest ascent, and Central Composite Design experiments are sequentially conducted. The simulation deviation of the resting angle of red clover seeds is employed as the evaluation criterion for parameter optimization. The results indicate that the coefficients of static friction between red clover seeds, the coefficients of rolling friction between red clover seeds, and the coefficients of static friction between red clover seeds and the steel plates significantly influence the resting angle. Modeling was performed using a backpropagation neural network, a genetic algorithm–optimized BP network, particle swarm optimization, and simulated annealing. It was found that GA-BP ensured both accuracy and stability. Compared to the traditional response surface methodology, GA-BP showed better fitting performance. For the optimized red clover seed simulation, the error between the angle of repose and the physical experiment was 0.98%. This research provides new insights into the calibration of small-grain seed parameters, demonstrating the value of GA-BP for precision modeling.

Greenhouses, as important parts of facility agriculture, can reduce the restrictions on agricultural production imposed by the natural environment and make rational and efficient use of production resources. We conducted long-term, continuous testing of temperature, humidity, light intensity, and CO₂ concentration parameters in a heliostat greenhouse in the central and western parts of the Inner Mongolia Autonomous Region, a cold and arid region of northern China. A large amount of data was processed by statistical observation, simulation analysis, and 3D reconstruction to obtain the overall distribution, variation pattern, and mathematical model of the regional greenhouse environment in time and space. The results show that the temperature, humidity, light intensity, and CO₂ concentration in the greenhouse have significant daily variation patterns, that there are strong coupling relationships between light intensity–CO₂ concentration–time and indoor temperature–light intensity–CO₂ concentration, that the coefficients of determination (R²) of the mathematical models are 0.88 and 0.89, and that the standard errors (RMSE) are 49.67 ppm and 45.30 ppm, respectively. The environmental parameters were fitted with high accuracy in order to provide scientific data for the cultivation of heliostats in the region.

In addressing the challenges of low pelletization qualification rates, poor uniformity between seeds and powder, and difficulties in optimizing equipment parameters for agropyron seed coating, this paper integrates numerical simulation with experimental verification to optimize the working parameters of pelletization coating. The study investigates the impact of vibration frequency, vibration direction, vibration amplitude, rotational speed, and inclination angle on seed-powder mixing uniformity and single-seed pellet qualification rates through both physical experiments and simulation tests. The study found that the coefficient of variation obtained through discrete element simulation can serve as a reliable surrogate indicator for evaluating pelletization coating quality, with its variation trend highly consistent with the single-seed pellet qualification rate observed in physical experiments. A secondary regression orthogonal design experiment used these indicators to establish a second-order regression equation, thereby performing single-objective optimization of the regression model. The results showed that the relative errors between simulation and physical test parameters were 1.24% for vibration frequency, 1.08% for coating pan rotational speed, and 0.17% for coating pan inclination angle. This demonstrates the high reliability of the coefficient of variation as a surrogate indicator for pellet qualification. With the optimized parameters, the qualification rate of single-seed pellets for agropyron seeds reached 95.3%, and the relative error between model predictions and physical tests was 1.7%. These findings validate the use of the second-order regression equation for predicting and analyzing single-seed pellet qualification rates and provide valuable insights for designing small-grain forage seed pelletization coating machines and optimizing coating parameters.