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Prediction of Top Coal Cavability Character of a Deep Coal Mine by Empirical and Numerical Methods

  • Mineral Mining Technology
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Abstract

Longwall top coal caving mining provides high productivity where thick coal seams exist. The study aims to predict the cavability character of the top coal for deep and thick coal seam in Soma lignite basin located at Western Turkey. Active longwall top coal caving mines are at a depth of 100–400 m and they were used for comparison purposes. New coal mining operations will be initiated in deep sectors of the basin in the future. Future longwall top coal caving operations will be unique under a depth of 700–1200 m with a varied thickness. Several empirical and numerical methods are utilized in the study. Pre-existing empirical approaches lack of essential data and additional numerical modeling is necessary to be employed in order to assess cavability character of the projected new mines. Numerical modeling provides a practical platform for construction of ground response curves. Existing mines and future mining operations can be evaluated and compared by ground reaction curves and a final conclusion about cavability character is reached.

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Acknowledgments

The author thanks Polyak Eynez Energy Mining A.Ş. and Fina Energy and its personnel for supporting the scientific research and providing necessary data for the study. The author personally thanks Geological Engineers of Polyak Eynez, Feridun Emre Yağımlı, Ali Türkoğlu, Mehmet Kılıç for providing extensive data on the geology of the area and Ahmet Erel who shared his extensive mining experience in Soma Basin.

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Authors and Affiliations

  1. Muğla Sıtkı Koçman University, Muğla, Turkey

    İ. F. Öge

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  1. İ. F. Öge
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Correspondence to İ. F. Öge.

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Published in Fiziko-Tekhnicheskie Problemy Razrabotki Poleznykh Iskopaemykh, 2018, No. 5, pp. 101–113.

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Öge, İ.F. Prediction of Top Coal Cavability Character of a Deep Coal Mine by Empirical and Numerical Methods. J Min Sci 54, 793–803 (2018). https://doi.org/10.1134/S1062739118054903

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  • DOI: https://doi.org/10.1134/S1062739118054903

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