Zeng Fandi, Liu Limin, Liu Yinzeng, Bai Hongbin, Li Chunxiao, Zhao Zhihuan
College of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan, 250100, China.
College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China.
Sci Rep. 2025 Jul 15;15(1):25629. doi: 10.1038/s41598-025-11827-9.
In order to calibrate the properties of the organic fertilizer particles, this work employs an integrated strategy that combines simulations, machine vision techniques, and physical experiments. Through physical testing, the fundamental physical characteristics of the organic fertilizer particles were identified. The initial analysis was through the Plackett-Burman test. The parameters that greatly influence the angle of repose are established. The previously identified important variables were optimized by the Central Composite Design test. The regression fitting models of the BP neural network have been developed from the data set derived from the Central Composite Design test results. Genetic algorithms (GA) and particle swarm optimization algorithms (PSO) were used to optimize the BP neural network. The RMAE and RMSE of the BP, GA - BP, PSO - BP and RSM regression models were compared and analyzed. The results showed that PSO - BP algorithm could achieve better fitting effect, and could construct a prediction model with higher accuracy and less error to analyze the repose angle of the organic fertilizer particles. The PSO - BP algorithm was used to iterate until the individual with the closest fitness was obtained. COR was 0.35, COS was 0.49, COS was 0.29 and COD was 0.38 were the optimal parameter combination.
为了校准有机肥颗粒的特性，本研究采用了一种综合策略，该策略结合了模拟、机器视觉技术和物理实验。通过物理测试，确定了有机肥颗粒的基本物理特性。最初的分析是通过Plackett-Burman试验进行的。确定了对休止角有重大影响的参数。通过中心复合设计试验对先前确定的重要变量进行了优化。基于中心复合设计试验结果得到的数据集建立了BP神经网络的回归拟合模型。采用遗传算法（GA）和粒子群优化算法（PSO）对BP神经网络进行优化。对BP、GA-BP、PSO-BP和RSM回归模型的RMAE和RMSE进行了比较分析。结果表明，PSO-BP算法能取得较好的拟合效果，能够构建精度更高、误差更小的预测模型来分析有机肥颗粒的休止角。使用PSO-BP算法进行迭代，直到获得适应度最接近的个体。COR为0.35、COS为0.49、COS为0.29和COD为0.38是最优参数组合。
