Selection of Thick Coal Seam Mining Method Using Analytic Hierarchy Process

Yetkin, Mustafa Emre; Özfırat, M. Kemal

doi:10.5935/2447-0228.20190071

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Developments in machine learning fields have created several new computer-aided data mining and hybrid approaches applicable for prediction problems. Artificial Neural Networks (ANN) have extensively been used to develop the nonlinear relationships between input parameters in mining and other geotechnical engineering systems [18][19][20][21]. A genetic algorithm (GA) is a robust stochastic approach for predicting various civil and mining problems.…”

Section: Liturature Surveymentioning

confidence: 99%

Mine subsidence prediction using gene expression programming based on multivariable symbolic regression

Rasouli

Shahriar

Madani

2021

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

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Accurate prediction of surface subsidence becomes a significant challenge for active industrial companies in coal mining fields due to the importance of the economic impacts of longwall mining-induced subsidence. This article explores a new variant of genetic programming, namely gene expression programming (GEP). The GEP-based method is utilized to present a new mathematical formula for subsidence prediction in longwall coal mining. The derived model includes both geometrical and geological variables. The data set consists of field measurements obtained through 37 longwall panels of Ulan coal mine, NSW, Australia. The GEP-based model concluded satisfactory subsidence prediction outcomes compared to other empirical methods such as NCB, DMR, ACARP, and IPM. The predictive ability of the GEP-based models, which captures the complex nonlinear effects of the critical factors on the magnitude of subsidence, resulted in a statistically significant improvement in predictive capacity compared to the aforementioned empirical methods. The sensitivity analysis results indicated that Panel width and cover thickness with 31% and 23% were the most influential parameters in the proposed model. Also, the extracted seam thickness, thick layer location, and thick layer thickness had 19%, 16%, and 11% impact on the GEP proposed model, respectively.

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Section: Liturature Surveymentioning

confidence: 99%

Mine subsidence prediction using gene expression programming based on multivariable symbolic regression

Rasouli

Shahriar

Madani

2021

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

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“…Many researchers have carried out a lot of risk assessment work in underground mining 15 , vehicle transportation 16 , 17 , belt conveyor 18 and other accidents, and have systematically summarized many models and methods suitable for underground mine risk assessment 19 , 20 , such as analytic hierarchy process(AHP) and fault tree analysis(FTA), support vector machine(SVM), TOPSIS and neural network methods. However, there has been relatively less research on hot work operations in underground mining, and traditional risk assessment methods have limited practicality in addressing ambiguity, complexity, and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive risk assessment method for hot work in underground mines based on G1-EWM and unascertained measure theory

Ding,

Tian,

Wang

2024

Sci Rep

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A risk assessment method for hot work based on G1-EWM and unascertained measurement theory was proposed to prevent hot work accidents in underground mines. Firstly, based on the risk influencing factors and classification criteria for underground hot work operations in mines, a single indicator measurement matrix was constructed using unascertained measurement theory; Secondly, a risk assessment index system for mine underground hot work operations was established. The combination weight coefficient of each index was determined using the order relationship analysis method (G1) and entropy weight method (EWM) and coupled with the single index measurement evaluation vector to calculate the multi-index comprehensive evaluation vector of the evaluation object; Finally, the model was validated and examined using engineering examples, and the evaluation level was determined using confidence identification criteria. The results showed that the proposed method, when used to evaluate the risk of hot work operations in tunnels and vertical shafts in metal mines, produces risk levels that are in line with reality III (Moderate Risk) for the vertical shaft and IV (High Risk) for the tunnels. The evaluation model results are consistent with the risk evaluation results the whole process of on-site hot work, which verifies the model feasibility. A unique strategy and method for risk management in hot work operations in underground mines is provided by the combination of weighting and unascertained measure models, which has theoretical and practical value. Future research could focus on refineing this model by exploring the applicability in diverse mining environments and integrating advanced analytical techniques to enhance the predictive accuracy and operational efficiency.

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Prioritisation of Environmental Improvement Projects in Deep-Level Mine Ventilation Systems

Swanepoel

Vosloo

Laar

et al. 2023

Mining, Metallurgy & Exploration

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Deep-level underground mining costs in South Africa are continuously rising due to the increased depth at which gold is being mined, resulting in a rise in virgin rock temperature and an increase in cooling requirements. Therefore, mines must evaluate various ventilation and cooling optimisation projects required to be implemented throughout their life of mine (LOM). This study develops a method to identify and prioritise large capital expenditure (CAPEX) underground environmental improvement projects, to improve thermal comfort underground. The solution presents a ‘multi-criteria decision-making’ (MCDM) process, incorporating the ‘analytic hierarchy process’ (AHP) and the ‘technique for order performance by similarity to ideal solution’ (TOPSIS) into the method to provide for scientific decision-making. These projects are evaluated based on four criteria, namely project risk, the impact on the underground thermal comfort, cost, and implementation time. This study’s solution uses ventilation simulations to determine the impact of the projects on the underground environment. The identification and evaluation of an environmental improvement project to best suit the current economic climate, while improving underground conditions and decreasing the safety risk is an essential aspect of the solution developed in this study. The method was implemented on a mechanised deep-level underground mine and showed that surface refrigeration is the most suitable alternative, which was implemented at the case study mine as validation of the developed solution. The study proves that the MCDM method is an adequate solution to incorporate subjective criteria into the decision-making process, resulting in a scientific and structured approach to making significant and complex decisions.

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Selection of Thick Coal Seam Mining Method Using Analytic Hierarchy Process

Cited by 5 publications

References 6 publications

Mine subsidence prediction using gene expression programming based on multivariable symbolic regression

Mine subsidence prediction using gene expression programming based on multivariable symbolic regression

A comprehensive risk assessment method for hot work in underground mines based on G1-EWM and unascertained measure theory

Prioritisation of Environmental Improvement Projects in Deep-Level Mine Ventilation Systems

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