Research on Prediction Accuracy of Coal Mine Gas Emission Based on Grey Prediction Model

Zeng, Jun; Li, Qinsheng

doi:10.3390/pr9071147

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…To improve coal-site methane as well as emissions of gases prediction, Zeng and Li [56] used Nantun. After selecting grey forecasting model GM (1, 1), projected and real values were compared across various working face regions.…”

Section: Prediction Of Methane Emission Using Ann -Literature Reviewmentioning

confidence: 99%

Management of methane emission in coal mines using artificial neural networks: A systematic review

Sundas Matloob,

Li Yang,

Sumaiya Bashiru Danwana

et al. 2024

Int. J. Sci. Res. Arch.

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Underground mines are responsible for a large number of the released methane worldwide. Our study paper can contribute to mitigating methane emissions, hence reducing the concentration about greenhouse emissions within the environment and mitigating the associated hazardous risks. In this study, more than 45 recent journals on Methane emission in Coal Mines were gathered from Web of Science, IEEE Xplore, ScienceDirect, and ResearchGate. A systematic review is accomplished of the past four years of various parts of Methane gas emission such as anthropogenic emission sources, gas emissions detection, prediction of methane using different technology and the Artificial intelligence projection model for methane emission. The outcomes reveal that since the methane emission management has obtained increasing attention over the past four years. This study also shows that big countries are using technology to control and utilize the methane emission to reduce the energy crisis. To decrease the coal mine injuries, academic understanding of underground methane management has increased, different technologies are integrated and support from various IT departments has amplified for the forecasting. In the future, the most critical task for coal mines risk assessment is to restore the worker's trust in mine safety, and the primary solution is to give more awareness to the underground management and workers through utilizing Artificial Intelligence (AI) mainly Artificial Neural Networks (ANN).

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Section: Prediction Of Methane Emission Using Ann -Literature Reviewmentioning

confidence: 99%

Management of methane emission in coal mines using artificial neural networks: A systematic review

Sundas Matloob,

Li Yang,

Sumaiya Bashiru Danwana

et al. 2024

Int. J. Sci. Res. Arch.

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“…GM(1,1) model is a prediction model used to analyze small samples and poor information (G. Liu et al, 2021). By accumulating original data to generate new sequences, the model establishes formulas to achieve data prediction (Zeng & Li, 2021). At the same time, the model, using the continuous gray differential model, makes up for the incomplete and inaccurate system information, which helps to ensure higher accuracy (X.…”

Section: Gm(11) Modelmentioning

confidence: 99%

Dynamic Evolution and Trend Analysis of the Coupling Coordination Between Sci-Tech Popularization and Sci-Tech Innovation in China

Wang,

Zhao,

Yang

et al. 2023

Sage Open

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Sci-Tech Popularization (STP) and Sci-Tech Innovation (STI) are two significant components to realize China’s innovation development. STI provides direction and content for STP, while STP creates an intense atmosphere for STI. By establishing two evaluation index systems of STP and STI, the STP index and the STI index of China and Chinese 31 provinces from 2010 to 2019 was calculated, and the STP index and the STI index from 2020 to 2024 were predicted. Finally, the relative development degree (RDD), the coupling coordination degree (CCD) and the dynamic degree of STP and STI were figured out. The results showed that: (1) From 2010 to 2024, the STP index and the STI index of China and Chinese 31 provinces showed a growing trend. The growth rate of STI was higher than that of STP, but the STP and the STI were low. (2) The RDD results showed that the development of STP lagged behind that of STI after 2018, and the gap between them showed a growing trend. (3) The CCD of STP and STI in China spanned three grades: moderate imbalance, slight imbalance, and approaching imbalance. The CCD in Chinese each province spanned five grades: severe imbalance, moderate imbalance, slight imbalance, approaching imbalance, reluctant coordination. The CCD was developing in a good direction, China and most Chinese provinces were still in an unbalanced level instead. Finally, suggestions on infrastructure, technical means, personnel, and social responsibility were put forward to promote the coordinated development between STP and STI in China.

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“…x * y<B P(x, y)log 2 P(x,y) P(x)P(y) dxdy log 2 min(x, y) (14) where P(x,y) is the joint probability distribution of x and y. P(x) and P(y) are the probability distributions of x and y, respectively. B is the maximum resolution.…”

Section: Performance Comparison and Analysismentioning

confidence: 99%

“…With the help of transfer learning, they proved that the LSTM model had a good generalization ability in roof-weighting prediction. Based on the time-series distribution of the working resistance of a support and the characteristics of complex working conditions, Pang et al [14] developed a classification modeling method for support loading based on a clustering algorithm. Additionally, Zeng et al [15] established a Prophet + LSTM model for pressure prediction on a mining face by integrating the working resistance data for adjacent supports using additional regression variables models.…”

Section: Introductionmentioning

confidence: 99%

An AGCRN Algorithm for Pressure Prediction in an Ultra-Long Mining Face in a Medium–Thick Coal Seam in the Northern Shaanxi Area, China

Gao,

Hu,

Liu

et al. 2023

Applied Sciences

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Due to the increase in the length of the mining face, the pressure characteristics and spatial distribution in fully-mechanized mining faces are different from those in typical mining faces, which leads to great challenges in roof management and the intelligent control of ultra-long mining faces. Taking the ultra-long mining face of a medium–thick coal seam in the northern Shaanxi mining area as an example and using field monitoring data for the working resistance of the hydraulic supports, a non-linear prediction method was used to extract the features of the dynamic data sequence of the working resistance of the hydraulic supports, and a deep learning method was used to establish a pressure prediction model for ultra-long mining faces based on the adaptive graph convolutional recurrent network (AGCRN) algorithm. In the proposed model, the supports in the fully mechanized mining face were regarded as the logic nodes of a topological structure, while the time-series resistance data for the supports were regarded as data nodes on a graph. The AGCRN model was used to determine the spatiotemporal relationship between the working resistance data of adjacent hydraulic supports, thereby improving the accuracy of the proposed model. The MAE and MAPE were employed as performance evaluation indices. When the node-embedding dimension was set to 10 and the time window was set to 16, the corresponding MAE and MAPE values of the prediction model were the minimum values. Compared with the reference models (i.e., the BP, GRU, and DCRNN models), the MAE and MAPE of the AGCRN model were 38.75% and 23.49% lower, respectively, indicating that the AGCRN model effectively demonstrates high accuracy in predicting the working resistance of supports. The AGCRN model was applied in the prediction of the working resistance of the supports of the ultra-long fully mechanized mining face. The results revealed that the working resistance of the supports in the lower and upper areas was relatively small along the strike, whereas the working resistance of the supports in the middle area was large, exhibiting a zoning pattern of “low-high-low” in terms of the average working resistance. In conclusion, the proposed model provides data references for the state of the hydraulic supports, pressure identification, and intelligent control of the ultra-long mining faces of the medium–thick coal seams in northern Shaanxi.

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Research on Prediction Accuracy of Coal Mine Gas Emission Based on Grey Prediction Model

Cited by 11 publications

References 40 publications

Management of methane emission in coal mines using artificial neural networks: A systematic review

Management of methane emission in coal mines using artificial neural networks: A systematic review

Dynamic Evolution and Trend Analysis of the Coupling Coordination Between Sci-Tech Popularization and Sci-Tech Innovation in China

An AGCRN Algorithm for Pressure Prediction in an Ultra-Long Mining Face in a Medium–Thick Coal Seam in the Northern Shaanxi Area, China

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