| 基于SAPSO-ELM的瓦斯涌出量分源预测及应用 |
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| 引用本文： | 任海峰,严由吉,吴青海.基于SAPSO-ELM的瓦斯涌出量分源预测及应用[J].煤田地质与勘探,2021,49(2):102-109. |
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| 作者姓名： | 任海峰 严由吉 吴青海 |
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| 基金项目： | 国家自然科学基金重点项目(51734007) |
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| 摘 要： | 为了提高瓦斯涌出量预测精度，针对瓦斯涌出量影响因素的多重相关性、复杂性等问题，结合主成分分析法和分源预测理论，对开采层、邻近层、采空区的瓦斯涌出量数据分别进行主成分分析降维，得到预测指标。针对极限学习机(ELM)存在的输入权值矩阵与隐含层阈值随机生成的问题，利用模拟退火粒子群算法(SAPSO)对极限学习机的参数寻优，将新疆某煤矿回采工作面瓦斯涌出量及影响因素作为SAPSO-ELM模型的输入进行训练，再利用训练好的SAPSO-ELM模型对陕西某煤矿回采工作面的瓦斯涌出量进行验证预测，并对比原始ELM模型的预测结果。结果表明，SAPSO-ELM模型的平均相对误差为3.45%，ELM模型的平均相对误差为8.81%，与ELM模型相比，SAPSO-ELM模型预测精度及效率均优于原始ELM模型。分源预测理论和主成分分析法的结合有效解决了多因素间的多重相关性并降低了预测模型的复杂度，SAPSO-ELM预测模型实现了瓦斯涌出量的快速精准预测，对预防瓦斯事故发生和保障煤矿安全高效开采具有较好的指导作用。
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| 关 键 词： | 瓦斯涌出量 分源预测 主成分分析法 极限学习机(ELM) 模拟退 |
| 收稿时间： | 2020-12-28 |
Different-source prediction of gas emission based on SAPSO-ELM and its application |
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| Abstract: | In order to improve the accuracy of gas emission prediction, in view of the multiple correlations and complexity of the influencing factors of gas emission, principal component analysis and separated source prediction theory were combined, the gas emission data of the mining layer, adjacent layer, and goaf were respectively subjected to principal component analysis to reduce dimensionality, and the predictor was obtained. Aiming at the problem that the input weight matrix and hidden layer threshold of the extreme learning machine were generated randomly, the simulated annealing particle swarm optimization algorithm was used to optimize the parameters of the extreme learning machine, and the gas in a coal mining face in Xinjiang was optimized. The output and influencing factors were used as the input of the SAPSO-ELM model for training, and then the trained SAPSO-ELM model was used to verify and predict the gas emission of a coal mining face in Shaanxi, and the prediction results of the original ELM model was compared. The results show that the average relative error of the SAPSO-ELM model is 3.45%, and the average relative error of the ELM model is 8.81%. Compared with the ELM model, the prediction accuracy and efficiency are better than the original ELM model. The combination of source prediction theory and principal component analysis effectively solves the multiple correlations among multiple factors and reduces the complexity of the prediction model. Meanwhile, the SAPSO-ELM prediction model realizes the rapid and accurate prediction of gas emission, which plays a guiding role in preventing gas accidents and ensuring safe and efficient mining of coal mines. |
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