Three-dimensional stochastic distribution characteristics of void fraction in longwall mining-disturbed overburden

Wang, Shaofeng; Liu, Kanghui; Wang, Shanyong; Liang, Yuntao; Tian, Fuchao

doi:10.1007/s10064-022-02929-z

Cited by 13 publications

(7 citation statements)

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“…The algorithms based on machine learning or deep learning are mainly used [85][86][87], including ANN in combination with the Levenberg-Marquardt Scheme having backpropagation [88,89], Shapley Additive exPlanations (SHAP) in combination with CatBoost (an AI model was used for increasing the gradients on decision trees) [90], two-dimensional regression models [91][92][93], Shapley Value Regression [94], Nearest Neighbor Method [95], etc. The main disadvantages of ANN, as well as of other "stochastic" interpolation methods, are provided in [96][97][98].…”

Section: Methodsmentioning

confidence: 99%

Reuse and Mechanochemical Processing of Ore Dressing Tailings Used for Extracting Pb and Zn

Golik,

Klyuev,

Martyushev

et al. 2023

Materials

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The increasing accumulation of rock waste obtained due to ore processing and its environmental impacts, such as acid mine drainage and elevated concentrations of heavy metals in soils, necessitates the transformation of mining technologies based on the concept of circular waste management. The research is aimed at improving the parameters of the mechanical activation effect produced on technogenic georesources, as well as at expanding the application scope of disintegrators in the field of using the partial backfill of the mined-out space when developing stratified deposits. In this regard, the research purpose was to substantiate the parameters of extracting metals from enrichment tailings using their mechanochemical activation to ensure cyclic waste management. The research involved the application of three-dimensional interpolation methods used for processing the data and the graphical representation. As a result, the following was found to be characteristic of the waste of the Sadonsky mine management. The degree of extracting zinc from pre-activated tailings increases logarithmically when the H2SO4 concentration and the NaCl proportion decrease 3.5 times. The degree of extracting lead from the activated tailings increases according to the Fourier law when decreasing the NaCl mass concentration, and an optimal range of the H2SO4 (0.38–0.51%) proportion decreases six times. One of the key results of the research is the justification of expanding the scope of applying disintegrators in the case of a directed activation influence exerted on the components of the stowing strips. The obtained results expand the understanding of the mechanism of the influence of the mechanochemical activation of dry tailings on the reactivity unevenness when extracting several metals from them.

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Section: Methodsmentioning

confidence: 99%

Reuse and Mechanochemical Processing of Ore Dressing Tailings Used for Extracting Pb and Zn

Golik,

Klyuev,

Martyushev

et al. 2023

Materials

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“…Recently, Wang's team from Central South University conducted an in-depth study on the void rate distribution in the rock layers and the surface under a mining and combustion disturbance. They obtained the void rate distribution models after horizontal and inclined coal seam mining by theoretical derivation, physical experiments, and numerical simulations [40,[52][53][54]. Their research results are of great significance for carrying out the study of multi-field coupling characteristics of underground coal fires and the study of fire prevention and suppression.…”

Section: The Effect Of Void Distributionmentioning

confidence: 99%

Complexity Study on Multi-Field Coupling Systems for Underground Coal Fires

Wang,

Guo,

Yang

2023

Sustainability

Self Cite

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Underground coal fires are a major disaster that needs to be urgently addressed in the coal mining industry, as they can cause waste of resources, environmental pollution, threaten the life and health of species, and cause serious damage to society and the economy. Currently, the research on the prevention and control of underground coal fires focuses more on the technical level and lacks scientific guidance at the methodological level. Based on this research gap, this paper uses CiteSpace 6.1.R6 to analyze the research hotspots and subjects in the field of underground coal fires and gives a comprehensive research overview of its microscopic chemical reaction substance and macroscopic multi-field coupling characteristics. Then, from the methodological level, it summarizes the complex system characteristics of underground coal fires and puts forward the elements and principles for the prevention and control of underground coal fire disasters under the paradigm of complex system research. This study proposes a new way of thinking for the prevention and control of underground coal fires and helps to build a whole-process prevention and control system.

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“…In 2022, Wang and his team assessed and predicted the deformation and subsidence process in a longwall mine using statistical distributions (Wang et al, 2022). Their simulation experimentsemploying deterministic and stochastic distribution models alongside discrete element numerical simulations-revealed that the distribution of the evacuated fraction of the Earth in the caved area assumes a U-shape, while in the subsidence and surface subsidence area, it takes an M-shape.…”

Section: Introductionmentioning

confidence: 99%

Land subsidence prediction in coal mining using machine learning models and optimization techniques

Jahanmiri,

Noorian-Bidgoli

2024

Environ Sci Pollut Res

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Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as troughs subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. We consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

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Three-dimensional stochastic distribution characteristics of void fraction in longwall mining-disturbed overburden

Cited by 13 publications

References 50 publications

Reuse and Mechanochemical Processing of Ore Dressing Tailings Used for Extracting Pb and Zn

Reuse and Mechanochemical Processing of Ore Dressing Tailings Used for Extracting Pb and Zn

Complexity Study on Multi-Field Coupling Systems for Underground Coal Fires

Land subsidence prediction in coal mining using machine learning models and optimization techniques

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