Evaluation of deep coal and gas outburst based on RS-GA-BP

Zhu, Junqi; Zheng, Haotian; Yang, Li; Li, Shanshan; Sun, Liankun; Geng, Jichao

doi:10.1007/s11069-022-05652-w

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…Due to the complex and diverse influencing factors of coal pillar retention parameters, the data-driven neural network model does not require an in-depth understanding of coal pillar retention parameters and can establish correlation patterns of relevant feature variables for similar geological formations through supervised learning of data samples. This approach exhibits good applicability and application prospects [39][40][41][42][43].…”

Section: Plos Onementioning

confidence: 99%

Calculation and program realization of coal pillar setting parameters in Huainan mining area

Yang

2024

PLoS ONE

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Coal pillar retention plays a crucial role in ensuring safety and minimizing ground deformation in coal mining operations. However, accurately and efficiently determining the optimal size of protective pillars, reducing coal pillar pressure, and addressing challenges such as limited access to retention parameters, lengthy observation times, and high labor costs are challenges that must be addressed. In this paper, we presented a methodology using Huainan mine as a case study to address these challenges. The solution involves deriving the formula for coal pillar retention parameters based on the Three Regulations definition and requirements. The total least squares algorithm was integrated with surface observation station data and the MATLAB software platform to automate the coal pillar retention solution. Furthermore, a linear regression model of coal pillar retention-related parameters was established using the geological mining condition data. The proposed ELM neural network model was optimized using a genetic algorithm and combined with the linear regression model to establish a predictive model. The results demonstrated that the proposed machine learning algorithm attains the requisite level of accuracy for industrial production.

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Section: Plos Onementioning

confidence: 99%

Calculation and program realization of coal pillar setting parameters in Huainan mining area

Yang

2024

PLoS ONE

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“…Numerous successful applications in geological disaster prediction, such as landslides (Zhang et al, 2019;Zhou et al, 2016), debris flow (Kern et al, 2017), and water inrush (Hu et al, 2013b;Dong et al, 2019;Shams et al, 2023;Ye et al, 2023), have demonstrated the efficacy of employing deep learning in scenarios involving similar mechanisms. To enhance the precision of evaluating oil-type gas emission disasters, a training model is proposed that combines Genetic Algorithm (GA) with Backpropagation (BP) neural network techniques (Zhu et al, 2023). Numerical simulation methods are employed to establish a model for oil-type gas migration, creating data samples for quantitative prediction by defining the relationship between factors influencing oil-type gas emission and the corresponding emission quantity.…”

Section: Prediction Of Oil-type Gas Emission Quantitymentioning

confidence: 99%

“…Through iterations involving selection, crossover, and mutation operations, an optimal individual is discerned. This optimal individual is subsequently utilized as the initial set of parameters for training the BP network, as illustrated in Figure 5(Zhu et al, 2023).…”

mentioning

confidence: 99%

Emission prediction of oil-type gas out of the coal seam floor based on integration of advanced electrical resistivity exploration techniques with the GA-BP

Mingyao,

Kang,

Haoqi

et al. 2024

Preprint

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Oil-type gas disasters are a recurrent concern in coal-oil-gas coexistence mines. To proactively anticipate the volume of oil-type gas emissions from floor rocks, this study introduces an investigative methodology to forecast the geological conditions of floor rocks ahead of the roadway face, leveraging the Direct Current (DC) method. The assessment of electrical resistance in rock formations, widely employed for identifying geological characteristics, serves as the basis for proposing a geological anomaly index derived from rock resistivity. This index effectively characterizes the stability of rock strata, providing an indirect assessment of fracture development. Serving as a real-time geological detection index for floor rocks located 100 meters ahead of the roadway face, it enhances predictive capabilities. Moreover, when amalgamated with parameters such as floor rock thickness and permeability, the paper presents simulations of oil-type gas emissions under varying geological conditions. Subsequently, an adaptive optimization of the Back Propagation (BP) neural network is achieved through the Genetic Algorithm Back Propagation Neural Network (GA-BP) model to evaluate the quantity of oil-type gas emissions in roadways. This advanced real-time prediction method is applied in Huangling coal mining to forecast oil-type gas emissions from the floor rocks in the excavation roadway area. Results demonstrate a congruence with field monitoring outcomes, affirming the accuracy of the predictive model. In conclusion, this advanced real-time prediction technique enables continuous monitoring and real-time forecasting of oil-type gas emissions ahead of roadways. This capability facilitates the implementation of specific measures for pre-extraction in gas disaster prevention and control, thereby ensuring the safety of coal mine production. Furthermore, the versatility of this advanced real-time prediction method extends to early warnings of rock mass instability-related disasters. Through a comprehensive understanding of subsurface conditions, continuous monitoring of changes, and the application of predictive models, timely actions can be undertaken to mitigate risks and uphold safety standards.

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“…The migration distance of cement slurry is influenced by factors such as the rheological properties of the slurry, gelation time, grouting pressure, and geometric characteristics of the receiving body (Zhu J. Q. et al, 2023). In cases where the particle size of the aggregate particles is uniform, theoretical formulas can be used for calculation.…”

Section: Introductionmentioning

confidence: 99%

Research on prediction of slurry migration distance in aggregate stacking based on GA-PSO-BPNN algorithm

Su,

Yang,

2023

Front. Earth Sci.

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After the water inrush accident in coal mine tunnels, early-stage pouring of aggregate forms a high-resistance, low-permeability aggregate stacking, transforming the pipeline flow into percolation. In the later stage, grouting is carried out into the interior of the aggregate stacking, effectively accumulating and solidifying the cement slurry. Among these, whether the slurry can migrate over long distances and fill the voids inside the aggregate stacking is the critical determinant of the success or failure of sealing. To quantitatively analyze the migration distance of slurry inside the aggregate stacking after grouting, a single-hole grouting test platform was established, and an orthogonal experiment was designed with grouting pressure, water cement ratio, and aggregate stacking porosity as influencing factors. Based on 25 sets of experimental measurements, four neural network prediction models suitable for studying the slurry migration distance within the aggregate stacking were constructed separately as back propagation neural network (BPNN), genetic algorithm (GA) combined BPNN, particle swarm optimization (PSO) combined BPNN, and GA-PSO combined BPNN. Evaluation criteria such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and the coefficient of determination (R2) were used for comparative analysis of the calculation errors and prediction accuracy of each model. From the perspective of neural network prediction results, the weight value of each influencing factor was analyzed, and the ranking was as follows: grouting pressure > aggregate particle size > water cement ratio, with grouting pressure being the primary controlling factor. The study demonstrates that the GA-PSO-BP model exhibits the best prediction performance, with an average relative error of only 1.59% and an R² of 0.998. This neural network model overcomes issues such as slow learning and getting stuck in tricky spots in BP neural networks. The prediction model shows high accuracy and stability, enabling more effective and accurate prediction of slurry migration distances, making it worthy of dissemination and application. This study can improve safety measures by reducing waste, expediting disaster management efforts, and minimizing environmental hazards associated with mining incidents.

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Evaluation of deep coal and gas outburst based on RS-GA-BP

Cited by 13 publications

References 23 publications

Calculation and program realization of coal pillar setting parameters in Huainan mining area

Calculation and program realization of coal pillar setting parameters in Huainan mining area

Emission prediction of oil-type gas out of the coal seam floor based on integration of advanced electrical resistivity exploration techniques with the GA-BP

Research on prediction of slurry migration distance in aggregate stacking based on GA-PSO-BPNN algorithm

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