Liu Shuang, Qu Enxiang, Lv Chun, Zhang Xueyuan
School of Architecture and Civil Engineering, Qiqihar University, Qiqihar, 161006, Heilongjiang, People's Republic of China.
Sci Rep. 2024 Oct 14;14(1):16804. doi: 10.1038/s41598-024-67139-x.
The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.
露天矿的爆破块度大小受多种因素影响，且这些影响因素之间存在非常复杂的非线性关系。传统的经验公式和单一神经网络模型无法满足现代爆破安全的要求。为提高爆破块度大小的预测精度，以别斯库杜克露天煤矿的实测数据作为训练和测试样本。选取岩石抗拉强度、岩石抗压强度和炮孔间距等七个因素作为预测模型的输入变量。将爆破破碎块度的平均尺寸X50作为预测模型的输出变量。采用核主成分分析（KPCA）对输入变量进行降维。选用甲虫触角搜索算法（BAS）对反向传播（BP）神经网络的初始权重和阈值参数进行优化。最终建立了基于KPCA - BAS - BP的露天煤矿爆破破碎块度预测模型。结果表明，该模型的平均相对误差为1.77%，均方根误差为1.52%。与未优化的BP神经网络以及采用人工蜂群算法（ABC）优化的BP神经网络模型相比，该模型具有更高的预测精度，更适合预测露天煤矿的爆破块度大小，为多因素影响下的爆破破碎块度预测提供了一种新方法，填补了相关理论研究空白，具有一定的实际应用价值。
