基于PCA-SAPSO-BP神经网络的瓦斯涌出量预测研究
Prediction of gas emission based on PCA-SAPSO-BP neural network
-
摘要: 为提高煤矿井下瓦斯涌出量预测效率和准确性，针对瓦斯涌出量影响因素的多重相关性、复杂性问题，提出使用主成分分析法对影响因素进行降维处理,针对BP神经网络收敛速度慢且易陷入局部最优解的问题,引入退火粒子群算法优化BP神经网络的权值和阈值；利用Matlab软件编写并构建了PCA-SAPSO-BP神经网络耦合算法对瓦斯涌出量进行预测；选取开滦钱家营煤矿瓦斯涌出量及其影响因子数据作为样本，使用BP神经网络模型、PSO-BP模型和SAPSO-BP模型对样本进行预测。结果表明：PCA-SAPSO-BP神经网络模型的预测平均相对误差为1.06%，PCA-PSO-BP模型为2.20%，PCA-BP模型为3.00%，SAPSO-BP模型为1.61%，PSO-BP模型为2.81%，BP模型为3.98%；预测模型的归一化均方误差为0.002 5，希尔不等系数为0.005 5，平均绝对误差为0.07 m3/min，判定系数为0.997 5，证明PCA-SAPSO-BP神经网络模型提高了BP模型瓦斯涌出量的预测精度。Abstract: To improve the efficiency and accuracy of gas emission prediction in coal mine, in view of the multiple correlation and complexity of the influencing factors of gas emission, the principal component analysis method is proposed to reduce the dimension of the influencing factors. Aiming at the problem that BP neural network has slow convergence speed and is easy to fall into local optimal solution, annealing particle swarm optimization algorithm is introduced to optimize the weights and thresholds of BP neural network. The coupling algorithm of PCA-SAPSO-BP neural network is compiled and constructed by using Matlab software to predict the gas emission. The data of gas emission and its influencing factors in Qianjiaying Coal Mine in Kailuan Group are selected as samples, and the samples are predicted by BP neural network model, PSO-BP model and SAPSO-BP model. The results show that the average relative error of PCA-SAPSO-BP neural network model is 1.06%, PCA-PSO-BP model is 2.20%, PCA-BP model is 3.00%, SAPSO-BP model is 1.61%, PSO-BP model is 2.81%, BP model is 3.98%; the normalized mean square error of the prediction model is 0.002 5, the Hill inequality coefficient is 0.005 5, the average absolute error is 0.07 m3·min-1, and the judgment coefficient is 0.997 5. It is proved that the PCA-SAPSO-BP neural network model improves the prediction accuracy of gas emission of BP model.
-
-
[1] 徐亮.我国煤炭开发建设现状与“十四五”展望[J].中国煤炭，2021，47（3）：44-48. XU Liang. The present situation and expectation of coal exploitation and construction in China’s 14th Five-Year Plan period[J]. China coal, 2021, 47（3）: 44-48.
[2] 吕千，傅贵，任建国，等．中美煤矿事故调查报告的对比研究[J]．煤矿安全，2020，51（4）：244-247. LYU Qian, FU Gui, REN Jianguo, et al. Comparative study of coal mine accident investigation reports in China and America[J]. Safety in Coal Mines, 2020, 51（4）: 244-247.
[3] 秦玉金，苏伟伟，姜文忠，等．我国矿井瓦斯涌出量预测技术研究进展及发展方向[J].煤矿安全，2020，51（10）：52-59. QIN Yujin, SU Weiwei, JIANG Wenzhong, et al. Research progress and development direction of mine gas emission prediction technology in China[J]. Safety in Coal Mines, 2020, 51（10）: 52-59.
[4] 苏文叔.矿井瓦斯涌出[J].煤炭工程师，1989（2）：7. [5] 于良臣.煤壁瓦斯泄出规律及其应用[J].煤矿安全，1981，12（9）：1-6. [6] 张子戌，袁崇孚．矿井瓦斯涌出量预测方法探讨[J].焦作矿业学院学报，1993，35（6）：78-81. [7] 吕伏，梁冰，孙维吉，等．基于主成分回归分析法的回采工作面瓦斯涌出量预测[J]．煤炭学报，2012，37（1）：113-116. LV Fu, LIANG Bing, SUN Weiji, et al. Gas emission quantity prediction of working face based on principal component regression analysis method[J]. Journal of China Coal Society, 2012, 37（1）: 113-116.
[8] 付华，史冬冬．基于IGA-LSSVM的煤矿瓦斯涌出量预测模型研究[J].中国安全科学学报，2013，23（10）：51-55. FU Hua, SHI Dongdong. Study on gas emission prediction model based on IGA-LSSVM[J]. China Safety Science Journal, 2013, 23（10）: 51-55.
[9] 董晓雷，贾进章，白洋，等．基于SVM耦合遗传算法的回采工作面瓦斯涌出量预测[J].安全与环境学报，2016，16（2）：114-118. DONG Xiaolei, JIA Jinzhang, BAI Yang, et al. Prediction for gas-gushing amount from the working face of stope based on the SVM coupling genetic algorithm[J]. Journal of Safety and Environment, 2016, 16（2）: 114-118.
[10] 肖鹏，谢行俊，双海清，等．基于KPCA-CMGANN算法的瓦斯涌出量预测研究[J].中国安全科学学报，2020，30（5）：39-47. XIAO Peng, XIE Xingjun, SHUANG Haiqing, et al. Prediction of gas emission quantity based on KPCA-CMGANN algorithm[J]. China Safety Science Journal, 2020, 30（5）: 39-47.
[11] 温廷新，靳露露．基于预处理的IFOA-ELM煤与瓦斯突出预测模型[J].中国安全科学学报，2020，30（1）：35-41. WEN Tingxin, JIN Lulu. IFOA-ELM prediction model of coal and gas outburst based on preprocessing[J]. China Safety Science Journal, 2020, 30（1）: 35-41.
[12] 蔺亚兵，秦勇，王兴，等．彬长低阶煤高瓦斯矿区瓦斯地质及其涌出特征[J].煤炭学报，2019，44（7）：2151-2158. LIN Yabing, QIN Yong, WANG Xing, et al. Geology and emission of mine gas in Binchang mining area with low rank coal and high mine gas[J]. Journal of China Coal Society, 2019, 44（7）: 2151-2158.
[13] 张瑞林，李东印，魏军，等．瓦斯涌出影响因素及其变化特征研究[J].煤炭科学技术，2005（10）：20-22. ZHANG Ruilin, LI Dongyin, WEI Jun, et al. Research on influenced factors and varied features of mine gas emission[J]. Coal Science and Technology, 2005（10）: 20-22.
[14] 赵伟，陈培红，曹阳．基于ACSOA-BP神经网络的瓦斯含量预测模型[J].煤矿安全，2022，53（1）：174-180. ZHAO Wei, CHEN Peihong, CAO Yang. Prediction model of coal seam gas content based on ACSOA optimized BP neural network[J]. Safety in Coal Mines, 2022, 53（1）: 174-180.
[15] 郭晨，夏玉成，孙学阳，等．高瓦斯矿井采煤工作面瓦斯地质分级评价方法与实践[J]．煤炭学报，2019，44（8）：2409-2418. GUO Chen, XIA Yucheng, SUN Xueyang, et al． Method and practice of gas geological grading evaluation on coal mining face of high gas mine[J]. Coal Science and Technology, 2019, 44（8）: 2409-2418.
[16] 李栋，孙振明，李梅，等．基于混沌粒子群的AWLSSVM瓦斯预测研究[J].煤矿安全，2020，51（8）：193-198. LI Dong, SUN Zhenming, LI Mei, et al. AWLSSVM gas prediction research based on chaotic particle swarm optimization[J]. Safety in Coal Mines, 2020, 51（8）: 193-198.
[17] 李鑫灵，袁梅，董洪，等．PSO－SVM模型在掘进工作面突出预警系统中的应用[J]．煤矿安全，2021，52（9）：90-95. LI Xinling, YUAN Mei, DONG Hong, et al. Application of PSO-SVM model in outburst warning system ofheading face[J]. Safety in Coal Mines, 2021, 52（9）: 90 -95.
[18] 汪吉林，翟建廷，秦勇，等．淮北许疃矿抽采后瓦斯含量损失影响因素分析及预测[J].煤炭学报，2019，44（8）：2401-2408. WANG Jilin, ZHAI Jianting, QIN Yong, et al. Influencing factors analysis and prediction of the loss of gas content after gas drainage in Xutuan Mine of Huaibei，China[J]. Journal of China Coal Society, 2019, 44（8）: 2401-2408.
[19] 王绅皓，谢婉丽，奚家米，等．基于 GIS 与 BP 神经网络的矿区塌陷易发性预测[J]．煤矿安全，2021，52（9）：218-223. WANG Shenhao, XIE Wanli, XI Jiami, et al. Prediction of mine collapse risk based on GIS and BP neural network[J]. Safety in Coal Mines, 2021, 52（9）: 218-223.
[20] 陈茜，黄连兵．基于LASSO-LARS的回采工作面瓦斯涌出量预测研究[J]．煤炭科学技术，2022，50（7）：171-176. CHEN Qian, HUANG Lianbing. Gas emission prediction from coalface based on least absolute shrinkage and selection operator and least angle regression[J]. Coal Science and Technology, 2022, 50（7）: 171-176.
-
期刊类型引用(3)
1. 乔兰，尹雅，李庆文，苗淼. 碳化硅增强相变充填体热力学性能研究. 岩土力学. 2024(12): 3624-3634+3644 . 
百度学术
2. 李佳帅，徐超. 外掺碳化硅混凝土力学性能的试验研究. 山西建筑. 2022(13): 119-121 . 百度学术
3. 宁波，闫艳，左夏伟，梁仁旺. 铁尾矿砂混凝土力学特性实验研究. 矿产综合利用. 2021(04): 159-164+175 . 百度学术
其他类型引用(3)
计量
- 文章访问数: 55
- HTML全文浏览量: 1
- PDF下载量: 42
- 被引次数: 6
下载:
