Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network.

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.

Prediction model of spontaneous combustion risk of extraction borehole based on PSO-BPNN and its application.

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.

Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment.

The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.

Mine water inrush source discrimination model based on KPCA-ISSA-KELM.

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.