Estimation of Ground Subsidence Deformation Induced by Underground Coal Mining with GNSS-IR

In this paper, GNSS interferometric reflectometry (GNSS-IR) is firstly proposed to estimate ground surface subsidence caused by underground coal mining. Ground subsidence on the main direction of a coal seam is described by using the probability integral model (PIM) with unknown parameters. Based on the laws of reflection in geometric optics, model of GNSS signal-to-noise (SNR) observation for the tilt surface, which results from differential subsidence of ground points, is derived. Semi-cycle SNR observations fitting method is used to determine the phase of the SNR series. Phase variation of the SNR series is used to calculate reflector height of ground specular reflection point. Based on the reflector height and ground tilt angle, an iterative algorithm is proposed to determine coefficients of PIM, and thus subsidence of the ground reflection point. By using the low-cost navigational GNSS receiver and antenna, an experimental campaign was conducted to validate the proposed method. The results show that, when the maximum subsidence is 3076 mm, the maximum relative error of the proposed method-based subsidence estimation is 5.5%. This study also suggests that, based on the proposed method, the navigational GNSS instrument can be treated as a new type of sensor for continuously measuring ground subsidence deformation in a cost-effective way

An Advanced Control Strategy Applying Reduced Order Extended Kalman Filter Algorithm for Permanent Magnet Synchronous Motor

In control system of permanent magnet synchronous motor, the estimation of rotor position is an essential issue. The classical Kalman filter algorithm has been previously proposed to estimate rotor position, but it is commonly subject to parameter nonlinear characteristics. This paper presents an advanced control method based on Reduced Order Extended Kalman Filter (ROEKF) to deal with control issue of PMSM in low-speed region. The EKF is able to address the nonlinear characteristic of permanent magnet synchronous motor, where the reduced-order matrix can reduce computational burdens. Then, simulation verification is performed in MATLAB/Simulink and PSIM. In addition, experiments are performed to validate the proposed control method in a three-phase inverter-fed drive system using gallium nitride (GaN) power device. The simulation and experimental results show that the proposed control algorithm can quickly and accurately track the rotor position of PMSM.</p

Extraction of Irregularly Shaped Coal Mining Area Induced Ground Subsidence Prediction Based on Probability Integral Method

Underground coal mining-induced ground subsidence (or major ground vertical settlement) is a major concern to the mining industry, government and people affected. Based on the probability integral method, this paper presents a new ground subsidence prediction method for predicting irregularly shaped coal mining area extraction-induced ground subsidence. Firstly, the Delaunay triangulation method is used to divide the irregularly shaped mining area into a series of triangular extraction elements. Then, the extraction elements within the calculation area are selected. Finally, the Monte Carlo method is used to calculate extraction element-induced ground subsidence. The proposed method was tested by two experimental data sets: the simulation data set and direct leveling-based subsidence observations. The simulation results show that the prediction error of the proposed method is proportional to mesh size and inversely proportional to the amount of generated random points within the auxiliary domain. In addition, when the mesh size is smaller than 0.5 times the minimum deviation of the inflection point of the mining area, and the amount of random points within an auxiliary domain is greater than 800 times the area of the extraction element, the difference between the proposed method-based subsidence predictions and the traditional probability integral method-based subsidence predictions is marginal. The measurement results show that the root-mean-square error of the proposed method-based subsidence predictions is smaller than 3 cm, the average of absolute deviations of the proposed method-based subsidence predictions is 2.49 cm, and the maximum absolute deviation is 4.05 cm, which is equal to 0.75% of the maximum direct leveling-based subsidence observation

Estimation of Ground Subsidence Deformation Induced by Underground Coal Mining with GNSS-IR

In this paper, GNSS interferometric reflectometry (GNSS-IR) is firstly proposed to estimate ground surface subsidence caused by underground coal mining. Ground subsidence on the main direction of a coal seam is described by using the probability integral model (PIM) with unknown parameters. Based on the laws of reflection in geometric optics, model of GNSS signal-to-noise (SNR) observation for the tilt surface, which results from differential subsidence of ground points, is derived. Semi-cycle SNR observations fitting method is used to determine the phase of the SNR series. Phase variation of the SNR series is used to calculate reflector height of ground specular reflection point. Based on the reflector height and ground tilt angle, an iterative algorithm is proposed to determine coefficients of PIM, and thus subsidence of the ground reflection point. By using the low-cost navigational GNSS receiver and antenna, an experimental campaign was conducted to validate the proposed method. The results show that, when the maximum subsidence is 3076 mm, the maximum relative error of the proposed method-based subsidence estimation is 5.5%. This study also suggests that, based on the proposed method, the navigational GNSS instrument can be treated as a new type of sensor for continuously measuring ground subsidence deformation in a cost-effective way

Alternative Soil Substrates Addition Cause Deterioration in Reclaimed Soil Macropore Networks at Eastern Mining Area, China

Minesoil profiles are reconstructed by alternative soil substrates worldwide. However, some substrates lack appropriate soil characteristics and negatively affect the minesoil functions, these negative impacts are largely caused by the deterioration of macropore structure. Nevertheless, the differences of typical substrate characteristics and their influence on the deterioration are unclear. Thus, we present a case study to analyze macropore number, size, connectivity, distribution, and soil permeability of RMSs with three substrates (MSW, YRS and RM), respectively, using industrial X-ray computed tomography. The results indicated that (1) filling of substrates made adverse variations for minesoils in macropore number, Ma, ED, τ and size distribution, and the RMS filled with RM had biggest difference in macropore parameters with NCS, followed by the MSW and YRS. (2) The variations of RMSs in macropore parameters were found to be dominated by a synthetic action of substrate texture, SBD and SOM, where SOM showed significant positive correlations with most macropore parameters other than IM, and clay content and SBD showed significant negative correlations. (3) The macropore network can be linked to SP, among various macropore parameters, Ma, AM, and Ma with ED > 600 μm had significant positive correlations with it. It is suggested that the filling substrates need to be reformed from improving the substrate texture, bulk density, and organic matter content

Habitats generated by the restoration of coal mining subsidence land differentially alter the content and composition of soil organic carbon.

The content and composition of soil organic carbon (SOC) can characterize soil carbon storage capacity, which varies significantly between habitats. Ecological restoration in coal mining subsidence land forms a variety of habitats, which are ideal to study the effects of habitats on SOC storage capacity. Based on the analysis of the content and composition of SOC in three habitats (farmland, wetland and lakeside grassland) generated by different restoration time of the farmland which was destroyed by coal mining subsidence, we found that farmland had the highest SOC storage capacity among the three habitats. Both dissolved organic carbon (DOC) and heavy fraction organic carbon (HFOC) exhibited higher concentrations in the farmland (20.29 mg/kg, 6.96 mg/g) than in the wetland (19.62 mg/kg, 2.47 mg/g) or lakeside grassland (5.68 mg/kg, 2.31 mg/g), and the concentrations increased significantly over time, owing to the higher content of nitrogen in the farmland. The wetland and lakeside grassland needed more time than the farmland to recover the SOC storage capacity. The findings illustrate that the SOC storage capacity of farmland destroyed by coal mining subsidence could be restored through ecological restoration and indicate that the recovery rate depends on the reconstructed habitat types, among which farmland shows great advantages mainly due to the nitrogen addition