Study of Gas Emission Quantity Prediction Based on Chaotic Time Series

Abstract:

The gas is one of the most important factors that endanger the security of the mine production. At present, the mine has only realized the real-time monitoring of gas, but not the forcasting of gas.There are some limitation of the traditional forcasting method, such as modeling subjectivism and statistical forcasting. This paper presents the gas emission forecasted method by using chaos time series forecasting,and on this basis to establish time-series forecasting models. The results show that the gas emission system has the chaotic property, using chaotic time series forecasting method to forecast the amount of gas emission is feasible, and the prediction accuracy is improved after improving some parameters algorithms in the chaotic time series prediction algorithm. The contents of this article has laid a theoretical basis for non-contact coal and gas outburst dynamic forecast, but also open up new ideas for in-depth analysis of the gas emission complex.

• Periodical: Applied Mechanics and Materials (Volumes 423-426)
• Pages: 2297-2300
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.423-426.2297
• Online since: September 2013
• Authors: Gao Hua Liao, Yu Juan Tang, Fang Dai
• Keywords: Chaotic Time Series , Gas Concentration, Gas Emission, Prediction

References

[4] 凌明清 . 二维数控转台控制算法及实验研究 [ 西安 ]. 西安电子科技大学 . 2006 ， 2.

DOI: 10.7498/aps.53.626

[1] Qiao Meiying, Qiao Jianjun, Tao Hui, Ma, Xiaoping. Mine workface gas emission time-series fractal properties[C]. 2011 International Conference on Electrical, Information Engineering and Mechatronics, EIEM 2011. Jiaozuo, Henan, China. pp: 1287-1296.

DOI: 10.1007/978-1-4471-2467-2_152

[2] ZHANG Tian-jun, SU Lin, QIAO Bao-ming. The Application of Optimized GM(1, 1) in Mine Gas Flow Volume Forecast[C]. 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring.

DOI: 10.1109/cdciem.2011.459

[3] LV Jinhu, LU Anjun. Analysis and application of chaotic time series[M]. Wuhan: Wuhan University press, (2002).

[3] 王家畴，位在林，宋芳 . 基于 PMAC 运动控制器的开放式数控系统研究 . 哈尔滨理工大学学报 . 2004 ， 10.

DOI: 10.7498/aps.49.641

[4] HE Jun, WANG Yungang, CHEN Xinsheng, JING Fuxing. Chaotic characteristics of gas dynamic emission prior to coal and gas outburst[J]. Journal of Liaoning Technical University (Natural Science). 2010, 29( 4): 529-532.

[5] Shi Tian. The Research of Gas Concentration Prediction Based on Chaotic Time Series [Xi'an]. Xi'an University of Science And Technology. 2009, 6.