Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA‐ELM Algorithm

Yang, Zhenhua; Hong-wei, Zhang; Sheng, Li; Fan, Chunhua

doi:10.1155/2020/1287306

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…According to the actual situation and the equipment conditions, the anti-outburst measure of the heading face is advanced drilling [26][27][28]. The layout of the advance drilling is shown in Figure 2 and involves drilling a number of holes in the coal formation in front of the working face while maintaining a certain advance distance between the holes.…”

Section: Local Anti-outbreak Measuresmentioning

confidence: 99%

Research on Joint Protection Layers and Gas Prevention Technology in Outburst Coal Seams

Zhang

2022

Sustainability

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Outburst closed distance coal seam distances are extremely difficult to mine, and are commonly involved in safety accidents in the process of mining. Based on the engineering background of the Wulan Coal Mine at the western margin of the Ordos Basin, this paper presents a theoretical analysis, engineering analogies, and numerical simulations to determine the optimal mining sequence for the Wulan Coal Mine #7, #8, #2, and #3 coal seams. Floor penetration drilling was used to eliminate outbursts in the #7 and #8 coal seams, and gas control in the #2 and #3 coal seams was achieved using ground drilling to pump and release the pressured gas. We established a comprehensive management technology system for gas emissions from the short-distance joint protection layer in the outburst coal seams of Wulan Coal Mine, where pressure relief and drainage were carried out by drilling through the floor and surface drilling pressure relief extraction. Through field tests, the pre-drainage rate of the #2 coal seam in the Wulan Coal Mine was 66.8%, and the pre-drainage rate of the #3 coal seam was 68.1%. This shows that protective layer mining of the #7 and #8 coal seams combined with surface drilling to extract pressure-relief gas can protect the #2 and #3 coal seams.

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Section: Local Anti-outbreak Measuresmentioning

confidence: 99%

Research on Joint Protection Layers and Gas Prevention Technology in Outburst Coal Seams

Zhang

2022

Sustainability

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“…BA [33] is a brand-new method using frequency tuning mode. It is a suitable optimization technique created by modeling bat features.…”

Section: Introductionmentioning

confidence: 99%

Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model

Yuan,

Liu,

et al. 2024

Shock and Vibration

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The current methods for early fault diagnosis of rolling bearing have some flaws, such as poor fault feature information and insufficient fault feature extraction capability, which makes it challenging to guarantee fault diagnosis accuracy. In order to increase the accuracy of fault diagnosis, it proposes a new fault diagnosis method based on enhanced Symplectic geometry mode decomposition with cosine difference factor and calculus operator (ESGMD-CC) and bat algorithm (BA) optimized extreme learning machine (ELM). The vibration signal is first decomposed into a number of Symplectic geometry components (SGCs) by SGMD. The number of iterations is reduced by the cosine difference factor, which also successfully separates the noise components from the effective components. The calculus operator is adopted to strengthen the weak fault features, making it simple to extract. The fault feature vectors are calculated by the power spectrum entropy-weighted singular values. Finally, the ELM model optimized by BA iteratively is performed as the final classifier for fault classification. The simulation and experiments demonstrate that the proposed method has a better degree of fault diagnostic accuracy and is effective at extracting the rich fault information from vibration signals.

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“…Zhenhua Yang et al introduced an improved residual gas content prediction method based on the drilling cutting index and the bat algorithm-optimized ELM. In comparison with the BPNN, SVM, and ELM, this novel method exhibits superior accuracy and effectively uncovers the nonlinear relationship between the drilling cutting index and residual gas content [21]. Liming Qiu et al established a protrusion risk prediction model based on a convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

Tian,

Ma,

et al. 2023

Applied Sciences

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With the rapid advancement of artificial neural network (ANN) algorithms, many researchers have applied these methods to mine gas prediction and achieved numerous research achievements. It is of great significance to study methods that can accurately predict the gas content for the prevention of gas disasters in mining areas. In order to enhance the accuracy, stability, and generalization capability of the gas content prediction model, the GASA-KELM prediction model was established using the GASA algorithm to improve the KELM initial parameter assignment method, and the prediction model based on BPNN and SVM was established under the same conditions. The experimental results show that the GASA-BPNN model failed to achieve the desired outcome within 800 iterations. On the other hand, the GASA-SVM and GASA-KELM models accomplished the goal in significantly fewer iterations, taking only 673 and 487 iterations, respectively. Moreover, the overall average relative errors of the cross-validated gas content predictions were 15.74%, 13.85%, and 9.87% for the three models, respectively. Furthermore, the total average variance of the test set was 3.99, 2.76, and 2.05 for the GASA-BPNN, GASA-SVM, and GASA-KELM models, respectively. As a result, compared with other ANN models, the GASA-KELM model demonstrates higher accuracy, stronger prediction stability, and generalization ability in the practical application. This novel model provides a basis for accurately predicting gas content and proposing effective regional gas management measures.

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Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA‐ELM Algorithm

Cited by 6 publications

References 35 publications

Research on Joint Protection Layers and Gas Prevention Technology in Outburst Coal Seams

Research on Joint Protection Layers and Gas Prevention Technology in Outburst Coal Seams

Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

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