基于KPCA-CMGANN算法的瓦斯涌出量预测研究  

Prediction of gas emission quantity based on KPCA-CMGANN algorithm

作　　者：肖鹏 谢行俊[1,2] 双海清 刘朝阳 王海宁 徐经苍 XIAO Peng;XIE Xingjun;SHUANG Haiqing;LIU Chaoyang;WANG Haining;XU Jingcang(College of Safety Science and Engineering,Xi’an University of Science&Technology,Xi'an Shaanxi 710054,China;Key Laboratory of Western Mine Exploitation and Hazard Prevention,Ministry of Education,Xi’an University of Science&Technology,Xi'an Shaanxi 710054,China;Shaanxi Chenghe Mining Co.,Ltd.,Chengcheng Shaanxi 715200,China)

机构地区：[1]西安科技大学安全科学与工程学院,陕西西安710054 [2]西安科技大学教育部西部矿井开采及灾害防治重点实验室,陕西西安710054 [3]陕西陕煤澄合矿业有限公司,陕西澄城715200

出　　处：《中国安全科学学报》2020年第5期39-47,共9页China Safety Science Journal

基　　金：国家自然科学基金资助(51774235,51904238);陕西省自然科学基础研究计划(2020JM-530,2019JQ-337);陕西省教育厅专项科学研究计划(19JK0534)。

摘　　要：为了精准预测瓦斯涌出量,针对绝对瓦斯涌出量非线性、时变性、复杂性等特点,提出采用核主成分分析法(KPCA)对影响因素进行降维处理;针对BP神经网络(BPNN)中存在的收敛速度慢和易陷入局部最优解的问题,采用压缩映射遗传算法(CMGA)优化BPNN;构建CMGA与BPNN的耦合算法(CMGANN),计算分析某低瓦斯矿井监测历史数据形成的样本集,建立KPCA-CMGANN预测模型;用KPCA-CMGANN预测模型和其他3种网络模型分别对煤矿现场数据进行预测。结果表明:KPCA-CMGANN预测模型在379个时间步长里达到收敛,4个回采工作面的瓦斯涌出量预测相对误差分别为0.58%、0.63%、0.57%和0.45%,平均相对误差仅为0.56%,预测精度和收敛速度均优于对比模型,可实现瓦斯涌出量的快速精准预测。In order to accurately predict gas emission quantity,considering the nonlinearity,timevarying characteristic and complexity of absolute gas emission,KPCA was proposed to conduct dimensionality reduction for influencing factors.Secondly,targeting at problems of BPNNs’slow convergence and tendency to fall into local optimal solution,CMGA was adopted to optimize BPNN.Then,a coupling algorithm CMGANN based on CMGA and BPNN was constructed to calculate and analyze sample sets formed by historical data of a low gas mine,and KPCA-CMGANN prediction model was established which together with three other network models were used to predict coal mine field data.The results show that KPCA-CMGANN model achieves convergence in 379 time steps,and relative errors of gas emission prediction for four working faces are 0.58%,0.63%,0.57%and 0.45%with an average relative error at only 0.56%.Its prediction accuracy and convergence speed are superior to comparative model,making it ready to predict gas emission amount accurately and quickly.

关 键 词：瓦斯涌出量预测 核主成分分析法(KPCA) 压缩映射遗传算法(CMGA) BP神经网络(BPNN) 样本集 

分 类 号：X936[环境科学与工程—安全科学]