Prediction Model of Gas Quantity Emitted from Coal Face Based on PCA-GA-BP Neural Network and Its Application
Abstract
Gas has always been a serious hidden danger in coal mining. The quantity of gas emitted from the coal face is affected bymany factors. To overcome the difficulty in accurately predicting the quantity of emission, a novel predictive model (PCA-GABP)based on principal component analysis (PCA), genetic algorithm (GA) and back propagation (BP) neural network wasproposed. The model was tested and applied in different coal seams at Panbei Coal Mine in Huainan, China, involving sixteentraining samples and four predicting samples. Results showed that: Gas emission quantity was significantly correlated withburial depth, gas content in the mining layer, gas content in the adjacent layer, and layer spacing. The correlations amongthese variables exceeded 60%. Linear regression analysis using the optimized model was affected by sample size anddiscreteness. The correlation coefficient (R) and maximum relative error (MRE) of the PCA-GA-BP model were 0.9988 and3.02%, respectively. The MRE of the optimized model was 70.2% and 53.2% smaller than that of the BP and GA-BP models,respectively. The conclusions obtained in the study provide technical support for the prediction of gas quantity emitted fromcoal face, and the proposed method can be used in other engineering fields.References
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in memristor-based oscillators using intelligent sliding mode control.
Journal of Engineering Science & Technology Review, 8(2), 2015.
[2] Zohre Fasihfar and Javad Haddadnia. Designing a fuzzy rbf neural
network with optimal number of neuron in hidden layer and effect of
signature shape for persian signature recognition by zernike moments
and pca. In Web Information Systems and Mining (WISM), 2010 International
Conference on, volume 1, pages 188–192. IEEE, 2010.
[3] Lü Fu, Bing LIANG, Wei-ji SUN, and Yan WANG. Gas emission quantity
prediction of working face based on principal component regression
analysis method [j]. Journal of China Coal Society, 1:027, 2012.
[4] Yuyang Gao, Chao Qu, and Kequan Zhang. A hybrid method based
on singular spectrum analysis, firefly algorithm, and bp neural network
for short-term wind speed forecasting. Energies, 9(10):757, 2016.
[5] Liu Gaofeng, Wang Huaxi, and Song Zhimin. Study on the mine gas
emission rate prediction based on gas geology and grey theory. International
Journal of Earth Sciences and Engineering, 9(1):327–332,
2016.
[6] Uneb Gazder and Nedal T Ratrout. A new logit-artificial neural network
ensemble for mode choice modeling: a case study for border transport.
Journal of Advanced Transportation, 49(8):855–866, 2015.
[7] Na Lin, WN Yang, and Bin Wang. Hyperspectral image classification
on kmnf and bp neural network. Computer Eng. Design, pages 2774–
2777, 2013.
[8] SHI Long-qing, TAN Xi-peng, WANG Juan, et al. Risk assessment
of water inrush based on pca-fuzzy-pso-svc. Journal of China Coal
Society, 1:167–171, 2015.
[9] Fangcheng Lü, Chunxu Qin, et al. Particle swarm optimization-based
bp neural network for uhv dc insulator pollution forecasting. Journal of
Engineering Science & Technology Review, 7(1), 2014.
[10] Klaus Noack. Control of gas emissions in underground coal mines.
International Journal of Coal Geology, 35(1):57–82, 1998.
[11] Kotaro Ohga, Sohei Shimada, and Eiji Ishii. Gas emission prediction
and control in deep coal mines. Mineral Resources Engineering, 9(02):
239–254, 2000.
[12] Sahan A Ranamukhaarachchi, Ramila H Peiris, and Christine
Moresoli. Fluorescence spectroscopy and principal component analysis
of soy protein hydrolysate fractions and the potential to assess
their antioxidant capacity characteristics. Food chemistry, 217:469–
475, 2017.
[13] Luo Ronglei, Liu Shaohua, and Su Chen. Garment sales forecast
method based on genetic algorithm and bp neural network. Journal
of Beijing University of Posts and Telecommunications, 37(4):39–43,
2014.
[14] Abouna Saghafi. A tier 3 method to estimate fugitive gas emissions
from surface coal mining. International Journal of Coal Geology, 100:
14–25, 2012.
[15] Bouhouche Salah, Mentouri Zoheir, Ziani Slimane, and Bast Jurgen.
Inferential sensor-based adaptive principal components analysis of
mould bath level for breakout defect detection and evaluation in continuous
casting. Applied Soft Computing, 34:120–128, 2015.
[16] Shi Shiliang, Song Yi, He Liwen, and Z Chuanqu. Research on determination
of chaotic characteristics of gas gush based on time series in
excavation working face of coal mine. Journal of China Coal Society,
31(6):58–62, 2006.
[17] Manwendra K Tripathi, PP Chattopadhyay, and Subhas Ganguly. Multivariate
analysis and classification of bulk metallic glasses using principal
component analysis. Computational Materials Science, 107:79–
87, 2015.
[18] Di-Yuan Tzeng and Roy S Berns. A review of principal component
analysis and its applications to color technology. Color Research &
Application, 30(2):84–98, 2005.
[19] Qi-junWang and Jiu-long Cheng. Forecast of coalmine gas concentration
based on the immune neural network model. Journal of the China
Coal Society, 33(6):665–669, 2008.
[20] Xiao-Lu WANG, Jian LIU, and Jian-Jun LU. Gas emission quantity
forecasting based on virtual state variables and kalman filter. Journal
of China Coal Society, 36(1):80–85, 2011.
[21] Chun-rong WEI, Yan-xia LI, Jian-hua SUN, Hong-wei MI, and Jun LI.
Gas emission rate prediction in coal mine by grey and separated resources
prediction method. Journal of Mining & Safety Engineering, 4:
029, 2013.
[22] Xiaoheng Yan, Hua Fu, and Weihua Chen. Prediction of coal mine gas
emission based on markov chain improving iga-bp model. Computer
Modelling and New Technologies., 18(9):491–496, 2014.
[23] BAI Yun-xiao. Research on coal mine gas emission forecasting model
based on neural network algorithm. Coal Technology, 11:050, 2012.
[24] HQ Zhu, WJ Chang, and Bin Zhang. Different-source gas emission
prediction model of working face based on bp artificial neural network
and its application. Journal of China Coal Society, 32(5):504–508,
2007.
Published
2017-11-01
How to Cite
QIU, J.W. et al.
Prediction Model of Gas Quantity Emitted from Coal Face Based on PCA-GA-BP Neural Network and Its Application.
Journal of Power Technologies, [S.l.], v. 97, n. 3, p. 169--178, nov. 2017.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1119>. Date accessed: 22 dec. 2024.
Issue
Section
Energy Engineering and Technology
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