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Gold Science and Technology ›› 2024, Vol. 32 ›› Issue (1): 75-81.doi: 10.11872/j.issn.1005-2518.2024.01.108

• Mining Technology and Mine Management • Previous Articles     Next Articles

Research on the Prediction of CO Concentration in Single-head Roadway Based on Curve Fitting and Neural Network

Changwei ZHOU(),Xianping XIE(),Xidong DU   

  1. Faculty of Land Resources Engineering，Kunming University of Science and Technology，Kunming 650000，Yunnan，China
  • Received:2023-07-31 Revised:2023-11-07 Online:2024-02-29 Published:2024-03-22
  • Contact: Xianping XIE E-mail:1655460339@qq.com;xxping@kmust.edu.cn

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Abstract:

In order to realize the prediction of CO concentration in the single-head roadway of the mine，based on the monitoring data of CO concentration in the heading face of the single-head roadway in the 1800 transport lane of Laochang tin mine in Yunnan Province.Firstly，the MATLAB curve fitting toolbox was used to fit the curve of the change of CO concentration with time in the single-head roadway，and the mathematical model of the change of CO concentration with time in the single-head roadway of the mine was established.Through the model，the time required for the CO concentration value in the single-head roadway of the mine to reach the CO concentration value required by the safety regulations was obtained.Then，the convolutional neural network time series prediction model（CNN model） and the BP neural network time series prediction model（BP model） were used to predict the CO concentration，and the two evaluation indexes of R2 and RMSE were compared.The results show that the BP neural network time series prediction model has the better prediction effect on the CO concentration of the single-head roadway，which provides an accurate and reliable theoretical basis for the monitoring and control of the CO concentration value of the single-head roadway in the mine.

Key words: single-head roadway, MATLAB, curve fitting, convolutional neural network, BP neural network, time series prediction

CLC Number: 

  • TD711.41

Fig.1

Time series diagram of CO concentration at a measuring point in a single-head roadway"

Table 1

Variation of CO concentration with time at a measuring point"

时间/sCO浓度/（×10-6）时间/sCO浓度/（×10-6）时间/sCO浓度/（×10-6）时间/sCO浓度/（×10-6）时间/sCO浓度/（×10-6）
09047401921 4801352 220922 96039
208867601951 5001352 240902 98037
408777802031 5201352 260923 00039
608848002051 5401352 280913 02039
808888202111 5601352 300903 04038
1007438401931 5801332 320903 06040
1207418601971 6001272 340923 08037
1407308801951 6201262 360863 10038
1607399001961 6401262 380833 12038
1807439201961 6601262 400843 14040
2007209401961 6801262 420843 16040
2205359601841 7001252 440823 18039
2405169801801 7201212 460843 20039
2605011 0001791 7401202 480783 22041
2804891 0201781 7601192 500763 24043
3004711 0401791 7801202 520753 26042
3204621 0601781 8001182 540763 28043
3402831 0801791 8201182 560753 30042
3602791 1001631 8401182 580753 32043
3802631 1201631 8601142 600753 34045
4002691 1401641 8801142 620683 36048
4202701 1601641 9001132 640683 38047
4402741 1801641 9201122 660673 40046
4601881 2001641 9401122 680693 42048
4801771 2201511 9601112 700683 44049
5001941 2401511 9801062 720673 46049
5202001 2601512 0001072 740563 48051
5402151 2801512 0201062 760533 50052
5602231 3001522 0401062 780523 52053
5802331 3201522 0601072 800563 54052
6001741 3401432 0801072 820503 56051
6201871 3601432 1001052 840483 58053
6401911 3801432 1201042 860423 60038
6602041 4001432 1401022 88038
6802101 4201432 1601012 90036
7002201 4401432 1801012 92037
7201841 4601372 2001002 94038

Fig.2

Schematic diagram of neural network principle"

Fig.3

Structure of BP neural network"

Fig.4

Prediction results of CNN neural network model"

Fig.5

Prediction results of BP neural network model"

Table 2

R2 and RMSE of each prediction model"

模型类型R2RMSE
CNN神经网络模型0.90564.4643
BP神经网络模型0.95792.9821
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