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Hierarchy of Parameters Influencing Cutting Performance of Surface Miner through Artificial Intelligence and Statistical Methods


Affiliations
1 CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826 015, India
2 Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, India
 

Applicability of a surface miner (SM) must be based on a careful assessment of intact rock and rock mass properties. A detailed literature review was made to identify different parameters influencing the performance of various types of cutting machines deployed in different parts of the world. The critical parameters influencing the production, diesel consumption and pick consumption of SM in Indian coal and limestone mines, were identified through artificial neural network (ANN) technique and screened by correlation coefficient analysis. Parameters that were common in both ANN and correlation analysis were grouped under critical category and others in semi-critical category.

Keywords

Artificial Neural Network, Intact Rock, Rock Mass, Surface Miner.
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  • Tiryaki, B. and Dikmen, A. C., Effects of rock properties on specific cutting energy in linear cutting of sandstones by picks. Rock Mech. Rock Eng., 2006, 39(2), 89–120.

  • Kahraman, S., Bilgin, N. and Feridunoglu, C., Dominant rock properties affecting the penetration rate of percussive drills. Int. J. Rock Mech. Min. Sci., 2003, 40, 711–723.

  • Kirsten, H. A. D., A classification system for excavation in natural material. Civil Eng. S. Afr., 1982, 293–307.

  • Fowell, R. J., Johnson, S. T. and Speight, H. E., Assessing the performance of cutting tools in rock materials. In International Symposium on Geotechnical Stability in Surface Mining, Calgary, November 1986, p. 161.

  • Prikryl, R., Some microstructural aspects of strength variation in rocks. Int. J. Rock Mech. Min. Sci., 2001, 38, 671–682.

  • Evans, I., Basic mechanics of the point attack pick. Colliery Guardian, May 1984, pp. 189–193.

  • www.wirtgen.com (accessed on 16 February 2013).

  • Evans, I., The force required for point attack picks. Int. J. Min. Eng., 1965, 2, 63–71.

  • Thuro, K., Drillability prediction – geological influences in hard rock drill and blast tunnelling. Geol. Rund., 1997, 86, 426–438.

  • Murthy, V. M. S. R., Munshi, B. and Kumar, B., Predicting roadheader performance from intact rock and rock mass properties – a case study. In Proceedings of National Seminar on Rock–Machine Interaction in Excavations, Banaras Hindu University, Varanasi, 7–8 March 2008, pp. 81–93.

  • Hadjigeorgiu, J. and Scolbe, M. J., Ground characterization for assessment of ease of excavation. In Proceedings of International Seminar on Mine Planning and Equipment Selection (eds Singhal, R. K. and Vavra, M.), Balkema, 1990, pp. 323–331.

  • Dey, K. and Ghose, A. K, Predicting ‘cuttability’ with surface miners – a rockmass classification approach. J. Mines Met. Fuels, 2008, 56(5), 85–92.

  • Atkinson, T., Selection of open pit excavating and loading equipment. Trans. Inst. Min. Metall. Section A, 1971, 101–129.

  • Zivor, R., Vilhelm, J., Rudajev, V. and Lokaicek, T., Measurement of P and S-wave velocities in a rock massif and its use in estimating elastic moduli. Acta Geod. Geom., 2011, 2(162), 157–167.

  • Origliasso, C., Cardu, M. and Kecojevic, V., Surface miners: evaluation of the production rate and cutting performance based on rock properties and specific energy. Rock Mech. Rock Eng., 2014, 47, 757–770.

  • Thuro, K. and Plinninger, R. J., Wear prediction in hard rock excavation using the CERCHAR Abrasive Index. In Rock Engineering – Theory and Practice (ed. Schubert, W.), 2004, pp. 599–604.

  • Murthy, V. M. S. R., Kumar, D., Jain, P. and Dash, A. K., Development of a cuttability index of surface miner for performance prediction in different geomining conditions. In International Symposium on Rock Mechanics and Geo-Environment in Mining and Allied Industries, Banaras Hindu University, Varanasi, 12–14 February 2009, pp. 156–171.

  • Roxborough, F. F., The role of some basic rock properties in assessing cuttability. In Proceedings of the Seminar on Tunnels, Wholly Engineered Structures, I. E. Aust./AFCC, Sydney, Australia, April 1987, p. 122.

  • Howarth, D. F. and Rowlands, J. C., Development of an index to quantify rock texture for qualitative assessment of intact rock properties. Geo. Test J., 1986, 9, 169–179.

  • Evans, I. and Pomeroy, C. D., The Strength, Fracture and Workability of Coal, Oxford Pergamon Press, Library of Congress Catalog Card No. 66-14657, 1966.

  • Roxborough, F. F. and Phillips, H. R., Applied rock and coal cutting mechanics. Australian Foundation Workshop Course 156/81, 1981.

  • Bilgin, N., Seyrek, T. and Shahria, K., Golden horn clean up contributes valuable data. Tunn. Tunn., 1988, 41–44.

  • Shimada, H. and Matsui, K., Prediction of drivage/drilling rate by using rock impact hardness number. In Proceedings of New Horizon in Resource Handling and Geo-Engineering (MMIJ/AusIMM Joint Symposium), Ube, Japan, October 1994, pp. 485–492.

  • Goktan, R. M. and Guneş, N., A comparative study of Schmidt hammer testing procedure with reference to rock cutting. Int. J. Rock Mech. Min. Sci., 2005, 42, 466–472.

  • Adebayo, B., Evaluation of cuttability of selected rocks in SouthWestern Nigeria. Aust. J. Tech., 2008, 12(2), 126–129.

  • Fowell, R. J. and Johnson, S. T., Rock classification and assessment for rapid excavation. In Strata Mechanics – Developments in Geotechnical Engineering (ed. Farmer, I. W.), Elsevier Scientific Publishing Company: Amsterdam, 1982, vol. 32, pp. 241–244.

  • Bilgin, N., Yazici, S. and Eskikaya, S., A model to predict the performance of roadheaders and impact hammers in tunnel drivages. In Proceedings of the Eurock 1996 on Prediction and Performance in Rock Mechanics and Rock Engineering (ed. Barla, G.), Balkema, Rotterdam, 1996, vol. 2, pp. 715–720.

  • Adebayo, B., Effect of textural characteristics of rock on bit wear. Aust. J. Tech., 2011, 14(4), 299–307.

  • Mammen, J., Saydam, S. and Hagan, P., A study on the effect of moisture content on rock cutting performance. In Coal Operators’ Conference (ed. Aziz, N.), University of Wollongong and The Aus Inst Min Metall, Illawarra Branch, Australia, 12–13 February 2009, pp. 340–347.

  • Kramadibrata, S. and Shimada, H., The influence of rock mass and intact rock properties on the design of surface mines with particular reference to the excavation of rock, Part-I, II and III. School of Civil Engineering, Curtin University of Technology, 1996.

  • Copur, H., Ozdemir, L. and Rostami, J., Roadheader application in mining and tunnelling industries. Min. Eng., 1998, 38–42.

  • Thuro, K. and Plinninger, R. J., Roadheader excavation performance – geological and geotechnical influences. In Ninth ISRM Congress Paris, Theme 3: Rock dynamics and tectonophysics/Rock cutting and drilling, 25–28 August 1999.

  • Kelles, S., Cutting performance assessment of a medium weight roadheader at Cayirhan coal mine. Master of Science Thesis, Department of Mining Engineering, The Graduate School of Natural and Applied Science, Middle East Technical University, 2005, p. 58.

  • Meena, P., Kumar, M., Jain, P. and Murthy, V. M. S. R., Performance analysis of surface miner in Indian coal mines – a case study. In Conference on Emerging Trends in Mining and Allied Industries, NIT, Rourkela, 2–3 February 2008, pp. 57–65.

  • Lislerud, A., Principles of mechanical excavation. Posiva 97–12, commissioned by Posiva Oy, Mikonkatu 15 A, FIN 00100 Helsinki, Finland, 1997, p. 244.

  • Barendsen, P., Tunneling with machine working on the undercutting principle. In Proceedings of South African Tunneling Conference (ed. Goodman, J. A.), The Technology and Potential of Tunnelling, 1970, pp. 53–58.

  • Franklin, J. A., Broch, E. and Walton, G., Logging the mechanical character of rock. Trans. Inst. Min. Metall. Section A, 1971, 1–9.

  • Singh, R. N., Denby, B., Egretli, I. and Pathon, A. G., Assessment of ground rippability in opencast mining operations. Mining Departmental Magazine, University of Nottingham, 1986, 38, 21– 34.

  • Farmer, I. W., Energy based rock characterization. In Proceedings of International Symposium on Application of Rock Characterization Techniques in Mine Design (ed. Karamis, M.), AIME, Littleton, USA, 1986, pp. 17–23.

  • Gehring, K. H., A cutting comparisons. Tunn. Tunn., 1989, 27–30.

  • Jones, I. O. and Kramadibrata, S., An excavating power model for continuous surface miner. Aust. Inst. Min. Metall., 1995, 17.

  • Dey, K. and Ghose, A. K., Selecting a surface miner – an algorithm. Mining Industry Annual Review for 2009. J. Mines, Met. Fuels, 2009, 57(9), 282–286.


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  • Hierarchy of Parameters Influencing Cutting Performance of Surface Miner through Artificial Intelligence and Statistical Methods

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Authors

A. Prakash
CSIR-Central Institute of Mining and Fuel Research, Dhanbad 826 015, India
V. M. S. R. Murthy
Department of Mining Engineering, Indian School of Mines, Dhanbad 826 004, India

Abstract


Applicability of a surface miner (SM) must be based on a careful assessment of intact rock and rock mass properties. A detailed literature review was made to identify different parameters influencing the performance of various types of cutting machines deployed in different parts of the world. The critical parameters influencing the production, diesel consumption and pick consumption of SM in Indian coal and limestone mines, were identified through artificial neural network (ANN) technique and screened by correlation coefficient analysis. Parameters that were common in both ANN and correlation analysis were grouped under critical category and others in semi-critical category.

Keywords


Artificial Neural Network, Intact Rock, Rock Mass, Surface Miner.

References





DOI: https://doi.org/10.18520/cs%2Fv112%2Fi06%2F1242-1249