瓦斯异常涌出混沌时间序列预测模型的建立

发布时间：2018-04-18 04:11

本文选题：瓦斯异常涌出 + 混沌时间序列　；参考：《中国煤炭》2017年08期

【摘要】：运用混沌理论对平煤十矿的实际瓦斯涌出数据进行了分析处理,采用基于关联积分的C-C方法确定了重构空间的时间延迟和嵌入维数,并对时间序列数据进行相空间重构,利用最小数据量法确定了时间序列的最大Lyapunov指数;运用混沌理论加权一阶局域预测方法,建立了混沌时间序列瓦斯异常涌出预测模型;并利用平煤十矿己15-24080掘进工作面31d的瓦斯实际浓度数据对该模型进行了预测效果检验。结果表明:时间序列的最大Lyapunov指数大于零,证明了时间序列数据具有混沌特征;模型中瓦斯异常涌出的预测发生时间和实际发生时间比较吻合,预测精度达93%。预测模型的可靠性为制定煤矿瓦斯防治措施和采取安全防护措施提供了理论依据。
[Abstract]:Based on the chaos theory, the actual gas emission data are analyzed and processed. The time delay and embedding dimension of the reconstruction space are determined by using the C-C method based on the correlation integral, and the phase space reconstruction of the time series data is carried out.The maximum Lyapunov exponent of time series is determined by the method of minimum amount of data, and the prediction model of abnormal gas emission of chaotic time series is established by using chaos theory weighted local prediction method.The prediction effect of the model was tested by using the actual gas concentration data of No. 15 ~ 24080 tunneling face in Pingshan No. 10 Coal Mine.The results show that the maximum Lyapunov exponent of the time series is greater than zero, which proves that the time series data have chaotic characteristics, and the predicted time of gas outburst in the model is in good agreement with the actual occurrence time, and the prediction accuracy is 93%.The reliability of the prediction model provides a theoretical basis for the formulation of coal mine gas prevention and control measures and the adoption of safety protection measures.
【作者单位】：中国矿业大学(北京)资源与安全工程学院;
【基金】：国家自然科学基金项目(51274206)
【分类号】：TD712.5

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