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Journal of the Southern African Institute of Mining and Metallurgy

On-line version ISSN 2411-9717
Print version ISSN 2225-6253

J. S. Afr. Inst. Min. Metall. vol.109 n.5 Johannesburg May. 2009

 

TRANSACTION PAPER

 

Reconstruction time of a mine through reliability analysis and genetic algorithms

 

 

M. Kumral

Inonu University, School of Engineering, Malatya, Turkey

 

 


SYNOPSIS

A mining system consists of many sub-systems such as drilling, blasting, loading, hauling, ventilation, hoisting and supporting. During mining operation, these sub-systems may experience various problems that stop the operation because of possible environmental, equipment and safety issues. In order to ensure delivery contracts in the required quality and safe mining medium, the operation should be, at least, performed in the specified reliability level of the system. If the system reliability decreases below the specified level, there will be safety and financial losses for the mining company. Therefore, the mine should be maintained by a reconstruction procedure to guarantee the operation continuity. Given that each sub-system has a different reliability function and maintenance cost, the determination of reconstruction time will be a complicated decision making problem. In this paper, the determination of reconstruction time is formulated as a nonlinear optimization problem and solved by genetic algorithms (GA). A case study was conducted to demonstrate the performance of the approach for an underground operation. The results showed that the approach could be used to determine the best action time.

Keywords: mining system; genetic algorithms; reconstruction time; reliability analysis


 

 

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References

1. METTAS, A. Reliability allocation and optimization for complex system. http://www.reliasoft.org/pubs/2000rm_087.pdf 2000; 2000, 6 p.,Accessed 28 January 2008        [ Links ]

2. KUMAR, U. and HUANG, Y. Reliability analysis of a mine production system-A case study. Proceedings of Annual Reliability and Maintainability Symposium. 1999. pp. 167-172.         [ Links ]

3. LOUIT, D.M. and KNIGHTS, P.F. Simulation of initiatives to improve mine maintenance, Mining Technology (Trans. IMM A) 2001, vol. 110, pp. 47-58.         [ Links ]

4. ROY, S.K., BHATTACHARYYA, M.M., AND NAIKAN, V.N.A. Maintainability and reliability analysis of a fleet of shovels. Transactions of the Institution of Mining and Metallurgy, Section A- Mining Technology, 2001. vol. 110, no. 2, pp. 163-171.         [ Links ]

5. VAGENAS, N. and NUZIAIE, T. Genetic algorithms for reliability assessment of mining equipment. Journal of Quality in Maintenance Engineering, 2001, vol. 7, no. 4, pp. 302-311.         [ Links ]

6. HALL, R.A. and DANESHMEND, L.K. Reliability Modelling of Surface Mining Equipment: Data Gathering and Analysis Methodologies. International Journal of Surface Mining, Reclamation and Environment, 2003, vol. 17, no. 3, pp. 139-155.         [ Links ]

7. VAGENAS, N., KAZAKIDIS, V., SCOBLE, M., and ESPLEY, S. Applying a maintenance methodology for excavation reliability. International Journal of Surface Mining, Reclamation and Environment, 2003, vol. 17, no. 1, pp. 4-19.         [ Links ]

8. BARABADY, J. and KUMAR, U. Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran. Reliability Engineering & System Safety, 2008, vol. 93, no. 4, pp. 647-653.         [ Links ]

9. VAN DYK, W. and KNOBJES, B. Design for maintenance. Journal of the South African Institue of Mining and Metallurgy, 2006, vol. 106, no. 10, pp. 671-672.         [ Links ]

10. ANSARI, N. and HOU, N. Computational intelligence for optimization, Kluwer Academic Pub., 1997        [ Links ]

11. DAVIS, L. Handbook of genetic algorithms. New York: V.N. Reinhold, 1991.         [ Links ]

12. HAUPT, R.L. and HAUPT, S.E. Practical genetic algorithms. John Wiley & Sons, 1998.         [ Links ]

13. GOLDBERG, D.E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Pub. Co., 1989.         [ Links ]

14. PAINTON, L.A and CAMPBELL, J.E. Genetic algorithms in optimization of system reliability, IEEE Transactions on Reliability, 1995, vol. 44, no. 2, pp. 172-178.         [ Links ]

15. REEVES, C.R. Genetic algorithms. Modern heuristic techniques for combinatorial problems, 1993, Blackwell Pub. pp. 151-188.         [ Links ]

16. PHAM, D.T. and KARABOGA, D. Intelligent Optimization Techniques, Springer, 2000.         [ Links ]

17. SIVANANDAM, S.N. and DEEPA, S.N. Introduction to Genetic Algorithms, Springer, 2008.         [ Links ]

18. DENBY, B. and SCHOFIELD, N. Open-Pit design and scheduling by use of genetic algorithms, Transactions of the Institution of Mining and Metallurgy Section A- Mining Technology, 1994, vol. 103, pp. A21-A26.         [ Links ]

19. KUMRAL, M. Optimal location of a mine facility by genetic algorithms, Transactions of the Institution of Mining and Metallurgy Section AMining Technology, 2005, vol. 113, no. 2, pp. A83-A88.         [ Links ]

20. KUMRAL, M. Genetic algorithms for optimization of a mine system under uncertainty, Production Planning & Control, 2004, vol. 15, no. 1, pp. 34-41.         [ Links ]

21. LIU, B. Uncertain Programming. John Wiley and Sons Inc., 1999.         [ Links ]

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