Prediction method of mining subsidence based on improved boltzmann function
A technology for mining subsidence and prediction methods, applied in prediction, genetic law, genetic model and other directions, which can solve the problems of low prediction accuracy of movement deformation and slow convergence of surface subsidence boundaries.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0142] Huainan Zhujidong Mine 1222(1) working face has an average mining height of 1.9m, a working face strike length of 805m, a dip width of 230m, an average mining speed of 3.7m / d, an average coal seam dip of 3°, a near-horizontal coal seam, and an average mining depth of 945m; the average thickness of the loose layer is 321m, the 1222 (1) working face adopts the comprehensive mechanized coal mining process, and the roof is managed by the caving method. The layout of the monitoring points on the working face is as follows Image 6 shown; the improved prediction model curve and the measured model curve comparison Figure 7 , Figure 8 As shown in the figure, the fitting errors of all measuring points, the subsidence at the boundary and the boundary fitting errors of horizontal movement are calculated respectively.
Embodiment 2
[0144] The 1613(1) working face of Huainan Guqiao Mine has an average mining height of 2.9m, a working face strike length of 1528m, a dip width of 251m, an average mining speed of 5.56m / d, an average coal seam dip of 3°, a near-horizontal coal seam, and an average mining depth. The average thickness of the loose layer is 420m. The 1613 (1) working face adopts the comprehensive mechanized coal mining technology, and the roof is managed by the caving method. The layout of the monitoring points on the working face is as follows Figure 9 shown; the comparison between the predicted model curve and the measured model curve is Figure 10 , Figure 11 shown.
[0145] The fitting parameter and the middle error of embodiment 1-2 are shown in table 1 below:
[0146] Table 1: Summary table of measured parameters of different prediction models
[0147]
[0148] In Example 1-2, the two mining areas belong to the mining under the huge thick loose layer. From the fitting effect of the...
Embodiment 3
[0150] The predicted results in the thick alluvial mining area show that the predicted results using the improved Boltzmann function model have higher prediction accuracy than the probability integral method model and the Boltzmann function model. The design of the station and the protection measures of the building are of great significance. The present invention studies the relationship between model parameters and geological mining conditions through the collected measured data under the thick loose layer in Huainan, and the established multiple linear regression model is as follows:
[0151] P a =β 0 +β 1 V 1 +…+β m V m
[0152] In the formula: β 0 , β 1 , ..., β m is the regression coefficient, V 1 , ..., V m for geological mining conditions.
[0153]On the basis of sorting out the surface mobile observation stations of multiple working faces, the parameters of the improved Boltzmann function model are obtained by using quantum genetic algorithm. The inversion...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More