School of Economics and Management, Anhui University of Science and Technology, Huainan 232000, China.
School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232000, China.
Int J Environ Res Public Health. 2022 Oct 8;19(19):12869. doi: 10.3390/ijerph191912869.
Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.
煤与瓦斯突出严重威胁深部煤矿的开采安全。对这些事件的风险等级进行评估，可以有效地防止深部煤矿安全事故的发生。深部煤与瓦斯突出具有高维、非线性、小样本等特点，本文构建了一种基于改进量子粒子群优化支持向量机（IQPSO-SVM）的深部煤与瓦斯突出风险评价方法，利用支持向量机（SVM）在解决小样本、高维、非线性问题方面的独特优势。改进量子粒子群优化（IQPSO）用于优化 SVM 的惩罚和核函数参数，可以解决粒子群优化（PSO）和量子粒子群优化（QPSO）在训练过程中的最优局部风险和早熟收敛问题。该算法还可以平衡算法设计中全局搜索和局部搜索之间的关系，提高数据拟合的并行性、稳定性、鲁棒性、全局最优性和模型泛化能力。实验结果证明，与标准 SVM、粒子群优化支持向量机（PSO-SVM）和量子粒子群优化支持向量机（QPSO-SVM）模型的测试结果相比，IQPSO-SVM 显著提高了深部煤矿煤与瓦斯突出风险评价的准确性。因此，本研究为基于风险预测的深部煤与瓦斯突出事故预防提供了新的思路，也为其他领域的高维非线性问题的科学评价提供了重要参考。本研究还可为深部煤矿煤与瓦斯突出事故的预防提供理论依据，帮助煤矿企业提高安全管理能力。
