Wang Wei, Cui Xinchao, Qi Yun, Xue Kailong, Liang Ran, Bai Chenhao
College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China.
School of Coal Engineering, Shanxi Datong University, Datong 037000, China.
Sensors (Basel). 2024 Apr 30;24(9):2873. doi: 10.3390/s24092873.
Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing.
准确测量煤层气渗透率有助于有效预防煤层气安全事故。为了更准确地预测渗透率，我们提出了IDBO-BPNN煤体瓦斯渗透率预测模型。该模型将改进的蜣螂算法（IDBO）与BP神经网络（BPNN）相结合。首先，集成正弦混沌映射、鱼鹰优化算法和自适应T分布动态选择策略，对DBO算法进行改进，提高其全局搜索能力。然后，利用IDBO优化BPNN中的权重和阈值，提高其预测精度，并在一定程度上降低过拟合风险。其次，基于瓦斯渗透率的影响因素，选取有效应力、瓦斯压力、温度和抗压强度作为耦合指标。利用SPSS 27软件，通过Pearson相关系数矩阵分析各指标之间的相关性。此外，采用核主成分分析（KPCA）对原始数据进行提取。然后，将原始数据划分为主成分数据作为模型输入。将IDBO-BPNN模型的预测结果与PSO-BPNN、PSO-LSSVM、PSO-SVM、MPA-BPNN、WOA-SVM、BES-SVM和DPO-BPNN模型的预测结果进行比较。通过比较评估KPCA提高模型预测精度的能力和IDBO-BPNN模型的性能。最后，利用山西某煤矿的数据对IDBO-BPNN模型进行了测试。结果表明，预测结果与实际值吻合较好，验证了模型的可靠性和稳定性。因此，IDBO-BPNN模型更适合于学术研究写作中煤层气渗透率的预测。
