Investigations of water inrushes from aquifers under coal seams

Zhang, Jincai

doi:10.1016/j.ijrmms.2004.11.010

Cited by 199 publications

(88 citation statements)

References 5 publications

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“…Moisture causes the decrease of ore's strength parameters and may cause swallet [11][12][13][14][15][16][17] . To assess cohesion and internal friction angle for iron-micaceous-martite and martite ores in the course of time, prof. R.E.…”

Section: Discussionmentioning

confidence: 99%

Geomechanical Models and Prognosis of Stress-strain Behavior of Rock Ore in Development of Unique Deposits of Rich Iron Ores Under Water-bearing Formations

Trushko¹,

Protosenya²

2015

Biosci., Biotech. Res. Asia

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This paper gives a brief mining and geological description of the Yakovlevo deposit of rich iron ores of the Kursk magnetic anomaly (Russia), which is one of the largest by explored reserves of high quality ores (9.6 billion mt) with iron content up to 69%. For the assessment of physical and mechanical properties of ores and enclosing rocks, a testing procedure was developed, and a non-linear geomechanical model for the assessment of stress-strain state of rock ore and rocks masses around workings was proposed on the basis of the study. Tangential stress concentration coefficients on working's contour were calculated and zones of rock ore mass's limit state were identified. The calculation data agree with those found by experiment, giving ratios between working's breadth and roof arch height. Stress criterion for assessment of mine rocks around workings was developed, expressing the ratio of calculated stresses and calculated strength of rock mass, and recommendations were given on choosing kinds and parameters of supports for various stability categories of mine rocks outcroppings on workings' contours.Key words: Iron ore, mines, mine rocks properties, geomechanical model, assessment of stability.Basic resources of Russia's iron ore are concentrated within the Kursk magnetic anomaly 1 . 90.5% of rich Kursk Magnetic Anomaly's (KMA's) ores by A+B+C 1 categories and 96.9% by A+B+C 1 +C 2 categories are concentrated in the Belgorod iron ore zone. It includes deposits of rich ores, unique in reserves and quality: Yakovlevo (9.6 billion mt), Gostischevo (10.9 billion mt), Bolshetroitskoye (1.5 billion mt), Razumnoye, etc. Average iron content in ore exceeds 60%. Deposits of rich iron ores of the Belgorod ore zone of the KMA are 500-1,000 m deep.The Yakovlevo deposit is one of the largest deposits of high quality ores by discovered reserves with the highest iron content and the lowest harmful impurities (sulfur, phosphorus, etc.). Maximal depth of iron ores from the daylight surface exceeds 500 meters. Slope angles of ore bodies vary within 65-75°; in the northern part of the deposit they do not exceed 50°. Thickness of ore chute near lying side of banded iron formations is 20-30 m, in middle part and hanging side it increases up to 100-200 m. Besides hat-shape rich iron ore occurrence, the ores forming V-shaped and sheetlike bodies going rather deep are developed in the deposit.The highest iron content is seen in ironmicaceous ores. In martite-hydrohematite and hydrohematite ores the iron content is lower. The iron content depends on the thickness of ore chute -increasing thickness, as a rule, causes iron 2880 TRUSHKO & PROTOSENYA, Biosci., Biotech. Res. Asia, Vol. 12(3), 2879-2888 (2015) content in ore to grow. In upper layers of deposits in the zone from several to 20-40 m thick, the iron content varies from 45 to 60%. The zone from 10-20 to 200-250 m in the middle area of the Yakovlevo deposit contains up to 60% of iron. In the footwall, on the border with banded iron formation 0.5-12 m thick, the iron content ...

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Section: Discussionmentioning

confidence: 99%

Geomechanical Models and Prognosis of Stress-strain Behavior of Rock Ore in Development of Unique Deposits of Rich Iron Ores Under Water-bearing Formations

Trushko¹,

Protosenya²

2015

Biosci., Biotech. Res. Asia

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“…In safe mining practice, an impermeable coal pillar is usually left in place to prevent groundwater or surface water from flowing into mining workings through the mininginduced fractured zone (He et al, 1991;Industry, 2004;Peng and Zhang, 2007;Peng, 1992;Yuan and Wu, 2003;Zhang, 2005;Zhang and Shen, 2004;Zhang and Peng, 2005;Zhang et al, 2002). The impermeable pillar enables successful seam extraction with neither water inrush nor excessive groundwater discharge into the mine.…”

Section: Impact Of Thick Alluvial Soil On a Fractured Water-conductinmentioning

confidence: 99%

“…Due to the importance of the FWCZ, there has been much interest in studies of the FWCZ in mining research (Bureau et al, 1983;Gui et al, 1997;He et al, 1991;Industry, 2004;Lu and Wang, 2015;Ma et al, 2013;Miao et al, 2011;Peng and Zhang, 2007;Peng, 1992;Qian et al, 2003Qian et al, , 2010Teng, 2011;Tu, 2004;Wu et al, 2008;Xu et al, 2010;Yang and Gao, 1982;Yuan and Wu, 2003;Yuan et al, 2015;Zhang, 2005;Zhang and Shen, 2004;Zhang and Peng, 2005). These studies show that the main factors influencing the height and distribution pattern of the FWCZ are the lithology of the strata, the coal mining method, and the thickness of the overlying strata.…”

Section: Impact Of Thick Alluvial Soil On a Fractured Water-conductinmentioning

confidence: 99%

Impact of thick alluvial soil on a fractured water-conducting zone: An example from Huainan coal mine, China

Zhou

et al. 2016

J. S. Afr. Inst. Min. Metall.

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“…Currently, water inrushes still occur all over the world, especially in coal producing countries such as United States, Australia, China, Poland and India [1][2][3][4]. Frequent water accidents, on the one hand, seriously affect miners' safety and on the other hand affect productivity.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Risk of Fault-Induced Water Inrush Using the Adaptive Neuro-Fuzzy Inference System

2017

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Sudden water inrush has been a deadly killer in underground engineering for decades. Currently, especially in developing countries, frequent water inrush accidents still kill a large number of miners every year. In this study, an approach for predicting the probability of fault-induced water inrush in underground engineering using the adaptive neuro-fuzzy inference system (ANFIS) was developed. Six parameters related to the aquifer, the water-resisting properties of the aquifuge and the mining-induced stresses were extracted as the major parameters to construct the ANFIS model. The constructed ANFIS was trained with twenty reported real fault-induced water inrush cases, and another five new cases were used to test the prediction performance of the trained ANFIS. The final results showed that the prediction results of the five cases were completely consistent with the actual situations. This indicates that the ANFIS is highly accurate in the prediction of fault-induced water inrush and suggests that quantitative assessment of fault-induced water inrush using the ANFIS is possible.

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Investigations of water inrushes from aquifers under coal seams

Cited by 199 publications

References 5 publications

Geomechanical Models and Prognosis of Stress-strain Behavior of Rock Ore in Development of Unique Deposits of Rich Iron Ores Under Water-bearing Formations

Geomechanical Models and Prognosis of Stress-strain Behavior of Rock Ore in Development of Unique Deposits of Rich Iron Ores Under Water-bearing Formations

Impact of thick alluvial soil on a fractured water-conducting zone: An example from Huainan coal mine, China

Predicting the Risk of Fault-Induced Water Inrush Using the Adaptive Neuro-Fuzzy Inference System

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