Prediction method of coal mine gas occurrence law based on multi-source data fusion

Jiao, Huice; Song, Wei; Cao, Ping; Jiao, Dengming

doi:10.1016/j.heliyon.2023.e17117

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…When utilizing gas injection displacement and flow enhancement to extract gas, it is crucial to assess the suitability of the tested coal seam. On the basis of a statistical analysis of numerous safety risk examples, and taking into account the risk factors affecting coal mine gas safety, as well as relevant literature on the causes of coal mine safety risks, 7,[23][24][25][26][27][28][29][30][31][32][33] the potential influencing factors have been identified as follows: thickness of coal (a), Protodyakonov's coefficient (b), water content (c), ash content (d), porosity (e), permeability (f), gas content (g), gas pressure (h), gas saturation (i), Langmuir pressure constant (j), and Langmuir volume constant (k).…”

Section: Analysis Of Influencing Factors Of the Co 2 /N 2 -Ecgd Techn...mentioning

confidence: 99%

“…According to the analysis in Section 2.2, there are many potential factors affecting the gas displacement effect of the coal body by gas injection, so it is necessary to judge the applicability of the test coal seam when applying the technology of increasing gas drainage by underground gas injection. The data of 29 cases 7,23–33 of the CO 2 /N 2 –ECGD technology in China (ignoring the difference of gas injection displacement technology, the default process parameters are the best) are obtained from the public report to explore the influence law of potential impact indicators on gas injection displacement effect (Table 1).…”

Section: Risk Assessment Model For the Effectiveness Of Co2/n2–ecgd T...mentioning

confidence: 99%

“…To predict the risk of coal and gas eruption, Wang et al 7 established the improved grey wolf optimizer-SVM model. ML was used to study the gas occurrence pattern by Jiao et al 23 For the prediction of coal spontaneous combustion risk, Wang et al 24 proposed the SSA-CNN model. However, it failed to address significant concepts such as the constraints of conventional assessment techniques in long-term and short-term forecasting methods, and the feasibility of ML in overcoming limitations.…”

Section: Introductionmentioning

confidence: 99%

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A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology

Wang,

et al. 2024

Energy Science & Engineering

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To solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention.

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Section: Analysis Of Influencing Factors Of the Co 2 /N 2 -Ecgd Techn...mentioning

confidence: 99%

Section: Risk Assessment Model For the Effectiveness Of Co2/n2–ecgd T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology

Wang,

et al. 2024

Energy Science & Engineering

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“…Junyi et al [ 33 ] developed an enhanced real-time target detection system for intelligent vehicles, employing a multi-source fusion method within the ROS Melody software (Melodic) development environment and the NVIDIA Xavier hardware platform (NVIDIA, Santa Clara, CA, USA). To boost the predictive accuracy and efficiency of coal mine gas generation patterns, Huice et al [ 34 ] proposed a method based on multi-source data fusion. Yajie et al [ 35 ] improved soil moisture measurement accuracy by integrating GNSS-IR technology with optical remote sensing, utilizing a multi-data fusion approach based on the Genetic Algorithm–Back-Propagation Neural Network (GAP-NN).…”

Section: Introductionmentioning

confidence: 99%

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Ma,

Zheng,

Liang

et al. 2024

Sensors

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The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

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Molecular characteristics of eight lignites based on the big data obtained from Orbitrap mass spectrometry

Huang,

Fan,

et al. 2024

Journal of the Energy Institute

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Prediction method of coal mine gas occurrence law based on multi-source data fusion

Cited by 5 publications

References 13 publications

A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology

A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Molecular characteristics of eight lignites based on the big data obtained from Orbitrap mass spectrometry

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Prediction method of coal mine gas occurrence law based on multi-source data fusion

Cited by 5 publications

References 13 publications

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Molecular characteristics of eight lignites based on the big data obtained from Orbitrap mass spectrometry

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Product

Resources

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A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology

A novel intelligent risk prediction model for the effectiveness of CO₂/N₂–ECGD technology