摘要
Monitoring and analysis of daily gas concentrations at a mining face is a vital task on safety production and security management in the coal-mining industry. This study addresses modeling and prediction of daily gas concentration variations based on the elliptic orbit model. The model describes the hourly variation in daily gas concentration by mapping its time-series into the polar coordinates to create its elliptic orbit trace for further analysis. Experiments show workability of the proposed method that daily gas concentration variation at a mining face of one coal mine in China is well described by the elliptic orbit model. Result analysis and performance comparison of the proposed elliptic orbit model with the classical AR model on the same prediction tasks indicate potentiality of the proposed elliptic orbit model,which presents a vivid approach for modeling and forecasting daily gas concentration variations in an intuitive and concise way.
Monitoring and analysis of daily gas concentrations at a mining face is a vital task on safety production and security management in the coal-mining industry. This study addresses modeling and prediction of daily gas concentration variations based on the elliptic orbit model. The model describes the hourly variation in daily gas concentration by mapping its time-series into the polar coordinates to create its elliptic orbit trace for further analysis. Experiments show workability of the proposed method that daily gas concentration variation at a mining face of one coal mine in China is well described by the elliptic orbit model. Result analysis and performance comparison of the proposed elliptic orbit model with the classical AR model on the same prediction tasks indicate potentiality of the proposed elliptic orbit model, which presents a vivid approach for modeling and forecasting daily gas concentration variations in an intuitive and concise way.
基金
supported by the Scientific Research Fund of Hunan Provincial Science and Technology Department (No. 2013GK3090)
the National Natural Science Foundation of China (Nos. 51374107 and 51577057)
the Research Fund of Hunan Provincial Natural Science Foundation (No. 13JJ8014)