Wei Wang

To predict the risk of water inrush from coal seam floor more effectively, a prediction model of water inrush risk level of coal seam floor based on KPCA-DBO-SVM is proposed. Firstly, the risk level of water inrush of coal seam floor is graded based on the influencing factors of water inrush from coal seam floor which are determined by data of water inrush accident and related literature, and probability of water inrush in working face. Secondly, Kernel Principal Component Analysis (KPCA) is used to reduce the dimension of high-dimensional features of the influencing factors, Then, results of feature extraction are input into the DBO-SVM model. Penalty parameters and kernel parameters of Support Vector Machine (SVM) are optimized by Dung Beetle Optimization algorithm (DBO). Next, these data is mapped to high-dimensional space by SVM to separate. In this way, water inrush risk level of coal seam floor is predicted. Finally, 94 groups of sample data are selected and divided into training set and test set. Prediction results of KPCA-DBO-SVM model are compared with these that DBO-SVM, PSO-SVM and PSO-BPNN models. The results show that the accuracy of KPCA-DBO-SVM model prediction is increased by 0.18, 0.12, 0.29 respectively; the macro precision is increased by 0.16, 0.11, 0.27 respectively; the macro recall rate is increased by 0.14, 0.10, 0.28 respectively; and the Macro-F₁ is increased by 0.15, 0.10, 0.28 respectively. The KPCA-DBO-SVM model is applied to three coal mine working face to verify the stability and universality of the model whose prediction results are consistent with the actual engineering situation. Therefore, KPCA-DBO-SVM model is suitable for the risk prediction of water inburst of coal seam floor.

In order to effectively solve coal mine water penetration accident and improve emergency rescue capability and evaluation accuracy, a emergency rescue capability evaluation model of coal mine water penetration accident is proposed, which combines an improved combination weighting method with the marine predator algorithm (MPA) optimizing BP neural network (BPNN). First of all, the evaluation index system of emergency rescue capability of coal mine water penetration accident is constructed, including four primary indicators, emergency rescue prevention capability, emergency rescue preparation capability, emergency rescue response capability, rehabilitation recovery capability, and sixteen secondary indicators. Secondly, the subjective and objective weighting of the evaluation indicators are determined by the best worst method (BWM) and the criteria importance though intercrieria correlation (CRITIC) method. Lagrange function is introduced to build a decision model, and combination weighting are obtained by coupling the subjective and objective weighting through the euclidean distance function. Thirdly, the combined weight value is used as the input of the MPA-BPNN model, and the expected value as the output for linear regression prediction. Finally, the model is applied in a coal mine in Shanxi and compared with the BPNN model, GA-BPNN model, and PSO-BPNN model. The results show that mean absolute error of MPA-BPNN model has decreased by 6.5%, 4.3% and 3.5% respectively compared with other models, which proves the effectiveness and accuracy of the model. Therefore, MPA-BPNN is applicable to the evaluation of emergency rescue capability for coal mine water penetration accident.

In order to ensure the safety of coal mine production, a mine water source identification model is proposed to improve the accuracy of mine water inrush source identification and effectively prevent water inrush accidents based on kernel principal component analysis (KPCA) and improved sparrow search algorithm (ISSA) optimized kernel extreme learning machine (KELM). Taking Zhaogezhuang mine as the research object, firstly, Na⁺, Ca²⁺, Mg²⁺, Cl^-, SO2- 4 and HCO- 3 were selected as evaluation indexes, and their correlation was analyzed by SPSS27 software, with reducing the dimension of the original data by KPCA. Secondly, the Sine Chaotic Mapping, dynamic adaptive weights, and Cauchy Variation and Reverse Learning were introduced to improve the Sparrow Search Algorithm (SSA) to strengthen global search ability and stability. Meanwhile, the ISSA was used to optimize the kernel parameters and regularization coefficients in the KELM to establish a mine water inrush source discrimination model based on the KPCA-ISSA-KELM. Then, the mine water source data are input into the model for discrimination in compared with discrimination results of KPCA-SSA-KELM, KPCA-KELM, ISSA-KELM, SSA-KELM and KELM models. The results of the study show as follows: The discrimination results of the KPCA-ISSA-KELM model are in agreement with the actual results. Compared with the other models, the accuracy of the KPCA-ISSA-KELM model is improved by 8.33%, 12.5%, 4.17%, 21.83%, and 25%, respectively. Finally, when these models were applied to discriminate water sources in a coal mine in Shanxi, and the misjudgment rates of each model were 28.57%, 19.05%, 14.29%, 23.81%, 9.52% and 4.76%, respectively. From this, the KPCA-ISSA-KLEM model is the most accurate about discrimination and significantly better than other models in other evaluation indicators, verifying the universality and stability of the model. It can be effectively applied to the discrimination of inrush water sources in mines, providing important guarantees for mine safety production.

Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing.

The feasibility and accuracy of the risk prediction of gas extraction borehole spontaneous combustion is improved to avoid the occurrence of spontaneous combustion in the gas extraction borehole. A gas extraction borehole spontaneous combustion risk prediction model (PSO-BPNN model) coupling the PSO algorithm with BP neural network is established through improving the connection weight and threshold values of BP neural network by the particle swarm optimization (PSO) algorithm. The prediction results of the PSO-BPNN model are compared and analyzed with that of the BP neural network model (BPNN model), GA-BPNN model, SSA-BPNN model and MPA-BPNN model. The results showed as follows: the average relative error of the PSO-BPNN model was 4.38%; the average absolute error was 0.0678; the root mean square error was 0.0934; and the determination coefficient was 0.9874. Compared with the BPNN model, the average relative error, average absolute error and root mean square error decreased by 9.35%, 0.1707 and 0.2056 respectively; and the determination coefficient increased by 0.1169. Compared with the GA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.19%, 0.0602 and 0.0821 respectively; and the determination coefficient increased by 0.0320. Compared with the SSA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 5.70%, 0.0820 and 0.1100 respectively; and the determination coefficient increased by 0.0474. Compared with the MPA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.50%, 0.0861 and 0.1125 respectively; and the determination coefficient increased by 0.0488, proving that the PSO-BPNN model is more accurate than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model as for prediction. When the PSO-BPNN model was applied to three extraction boreholes A, B, and C in a coal mine of Shanxi, the prediction results were better than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model, proving the accuracy and stability of the PSO-BPNN model in predicting risk of borehole spontaneous combustion in other mine.

The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can be taken to avoid the occurrence of fires. In order to accurately predict the limit parameters of coal spontaneous combustion, the prediction model of coal spontaneous combustion limit parameters based on GA-SVM was constructed by coupling genetic algorithm (GA) and support vector machine (SVM). Meanwhile, the GA and particle swarm optimization algorithm (PSO) were used to optimize the back propagation neural network (BPNN) to construct the GA-BPNN and PSO-BPNN prediction models, respectively. To predict the intensity of air leakage of the upper limit of coal spontaneous combustion in the goaf, the prediction results of the models were compared and analyzed using MAE, MAPE, RMSE, and R² as the prediction performance evaluation indexes. The results show that the MAE of the GA-SVM model, the PSO-BPNN model, and the GA-BPNN model are 0.0960, 0.1086, and 0.1309, respectively; the MAPE is 2.46%, 3.11%, and 3.69%, respectively; the RMSE is 0.1180, 0.1789, and 0.2212, respectively; and the R² is 0.9921, 0.9818, and 0.9722. The prediction results of the GA-SVM model are the most optimal in four evaluation indexes, followed by the PSO-BPNN and the GA-BPNN models. Applying each model to the prediction of minimum residual coal thickness in the goaf of a coal mine in Shanxi, the GA-SVM model has higher accuracy, which further verifies the universality and stability of the model and its suitability for the prediction of coal spontaneous combustion limit parameters.

In order to quickly and accurately predict borehole spontaneous combustion danger and avoid borehole spontaneous combustion fires, a borehole spontaneous combustion prediction model combining the Hunger Games search optimization algorithm (HGS) and Random Forest (RF) algorithm was introduced. The number of trees and the minimum number of leaf nodes in RF were optimized by HGS. Based on the data obtained from the temperature rise experiment of spontaneous combustion characteristics in a Shandong mine laboratory, O₂, CO, C₂H₄, CO/∆O₂ and C₂H₄/C₂H₆ were selected as the input indexes for the prediction of borehole spontaneous combustion, and the spontaneous combustion temperature was selected as the output indexes to train the built model. The prediction performance and accuracy of the model were evaluated using four indexes: the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). At the same time, the prediction results of the HGS-RF model were compared with those of the RF model, Sparrow search algorithm (SSA) optimization RF model, particle swarm optimization RF model (PSO) optimization RF model and quantum particle swarm optimization RF model (QPSO) optimization. The results showed that the MAE of the RF, SSA-RF, PSO-RF, QPSO-RF and HGS-RF model samples were 17.541, 15.7752, 12.5903, 6.8594 and 6.6921, respectively. MAPE was 13.81%, 10.9766%, 9.6802%, 4.5731% and 5.1536%, respectively. RMSE values were 21.5646, 15.2017, 17.0091, 11.9879 and 12.1691, respectively. The R2 values were 0.9043, 0.9315, 0.9266, 0.9668, and 0.9717, respectively. At the same time, the reliability of the HGS-RF model was supplemented by taking the test data of the Zhengjia1204 coal mining face as an example. Finally, the model was applied to the prediction of borehole spontaneous combustion in the Jinniu Coal Mine, Shanxi Province. The prediction results show that the HGS-RF model can predict the spontaneous combustion temperature of different boreholes quickly and accurately. The results show that the HGS-RF model is more universal and stable than other models, indicating that the HGS-RF model is more suitable for the prediction of borehole spontaneous combustion.

In order to effectively prevent the spontaneous combustion of residual coal in goaf, taking the 10,101 fully mechanized top coal caving face of Baozigou coal mine as research object, the multi-field coupling numerical model of nitrogen injection in goaf is established. The FLUENT software is used to study the variation law of spontaneous combustion zone in goaf under dynamic mining of working face with different nitrogen injection parameters, determining the range of spontaneous combustion zone in stable stage. The fitting curve between nitrogen injection parameters and width of spontaneous combustion zone in goaf is obtained. Results show that with the increase of nitrogen injection depth from 10 to 60 m, the width of spontaneous combustion zone in goaf begins to decrease gradually, yet the width of spontaneous combustion zone tends to expand after more than 40 m. When the nitrogen injection location is 40 m, the spontaneous combustion zone width decreases from 49 to 22 m as the nitrogen injection volume increases from 500 to 1000 m³/h. Nitrogen injection continuously reduces the area of high temperature zone and temperature extreme value. When the nitrogen injection parameter is set to (40 m–1000 m³/h), temperature extreme value decreases by 308.85 K compared with that without nitrogen injection. When the nitrogen injection parameter is (40 m–690 m³/h), it can meet the inert cooling requirements of goaf. The width of spontaneous combustion zone is 31 m and the temperature extreme value is 309.95 K at the moment. Finally, engineering application of the fire prevention technology combining shot-off loss wind and nitrogen injection is used to test effect of spontaneous combustion prevention and verify accuracy of nitrogen injection simulation. CO concentration at the measuring point 1, 3 and 5 are reduced to 0 × 10⁻³‰, 2 × 10⁻³‰ and 1.2 × 10⁻³‰, and temperature are reduced to 295.15 K, 296.15 K and 295.65 K respectively. It shows that the spontaneous combustion of residual coal in goaf has been successfully controlled.

The fracture development of the overlying strata after coal mining is an important guarantee of efficient gas drainage. In order to explore the fracture evolution characteristics close to a mined coal seam group, the F_15.16−24130 working face in the Pingdingshan No. 10 coal mine was taken as the research background. The FLAC3D numerical simulation software was used to study the migration and failure characteristics of the overlying strata during mining of a coal seam group, and the fracture evolution process of the stope was investigated. The results show that as the advancing distance increased, the fracture density and fracture height increased continuously due to deformation and failure of the overlying rock. The displacement of the overlying rock initially increased and then decreased, and the displacement of the floor rock initially decreased and then increased. When working face F_15.16 of the coal seam advanced to 75 m, a saddle-shaped plastic zone gradually formed in the upper part of the goaf and the floor of the goaf was formed. The pressure relief depth was proportional to the advancement distance. As the advancement distance of the working face increased, the pressure relief depth gradually extended to the F₁₇ coal seam, which was conducive to the development and penetration of the fractures in the coal floor and rock mass and was convenient for pressure relief gas drainage from the F₁₇ coal seam.