Skip to main content

Advertisement

SpringerLink
Log in

Multi criteria genetic algorithm for optimal blending of coal

  • Application Article
  • Published:
OPSEARCH Aims and scope Submit manuscript

Abstract

The present paper deals with the optimal planning for blending of coal of different grades for beneficiation of coal with a view to satisfy the requirements of the end users with desired specifications. The input specifications are known whereas aspiration levels of the characteristics washed coal have been specified. Beneficiation of coal refers to the production of wash coal from raw coal with the help of some suitable beneficiation/coal washing technologies. The processed coal is used by the different steel plants to serve their purpose during the manufacturing process of steel. The aim is to fix the level of the raw coal samples from different coal seams to be used/fed for the beneficiation to meet the desired target of the coal blending indicators, yield to maximum extent and to restrict the input cost of raw coal to be fed for beneficiation. The problem is considered as multi criteria decision making problem and solved using multi objective genetic algorithm. A case study from a regional coal company situated at Jharia coalfield, India has been made and solved using the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Erarslan, K., Aykul, H., Akçakoca, H., Çetin, N.: Optimal blending of coal by linear programming for the power plant, 17th International Mining congress and Exhibition of Turkey, IMCET (2001)

  2. Kumral, M.: Application of chance constrained programming based on multi objective simulated annealing to solve a mineral blending problem. Eng. Optimiz. 35 (6) (2003).

  3. Shih, J.-S., Frey, H.C.: Optimal blending optimization under uncertainty. Eur J Oper Res 83(3), 452–465 (1995)

    Article  Google Scholar 

  4. Guo, X.-j., Chen, M., Wu, J.w.: Coal blending optimization of coal preparation production process based on improved GA , The 6th international conference on mining science and technology, Procedia Earth and Planetary Science, 1 654–660 (2009)

  5. Adeleke, A.A., Onumanyi, P., Ibitoye, S.A.: Mathematical optimization of non-coking inclusion in coking blend formulations. Petroleum Coal 53(3), 212–217 (2011)

    Google Scholar 

  6. Holland, J.H.: Adaptation in nature and artificial systems. University of Michigan Press, Ann Arbor (1976)

    Google Scholar 

  7. Goldberg, D.E.: Genetic Algorithms in search, optimization and machine learning. Addision- Wesley, (1989)

  8. Deb, K.: Optimization for Engineering Design: Agorithms and Concepts. Prentice- Hall, India (1995)

    Google Scholar 

  9. Deb, K.: Nonlinear goal programming using multiobjective genetic algorithms. J. Oper. Res. Soc. 52, 291–302 (2001)

    Article  Google Scholar 

  10. Deb, K.: Multi Objective Optimization by Genetic Algorithms. Wiley (2001).

  11. Deb, K., Goldberg, D.E.: An investigation of Niche and species formation in Genetic Function Optimization, Proceedings of the third Int’l Conference on GA . Morgan- Kaufmann, 97–106 (1989)

  12. Azadivar, F., Tompkins, G.: Simulation Optimization with qualitative variables and structural model changes a genetic programming approach. Eur. J. Oper. Res. 113, 169–182 (1999)

    Article  Google Scholar 

  13. Chu, P.C., Beasley, J.E.: A genetic algorithm for the multidimensional knapsack problem. J. Heuristics. 4, 63–86 (1998)

    Article  Google Scholar 

  14. Kochhar, J.S., Foster, B.T., Heragu, S.S.: Hope a genetic algorithm for the unequal area facility layout problems. Comp. Oper Res. 125, 583–594 (1998)

    Article  Google Scholar 

  15. Rosenberg, R.S.: Simulation of Genetic populations with biological properties. Ph.D. Thesis, University of Michigan, (1967).

  16. Schaffer, J.D.: Some experiments in Machine learning using Vector Evaluated Genetic Algorithms (TCGA file No. 00314). Ph.D Thesis. Vanderbilt University. (1984).

  17. Schaffer, J.D.: Multiple Objective Optimization with vector evaluated genetic algorithms. Grefenstette, J. J. (ed.) 93–100 (1985).

  18. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148.19 (1995)

    Google Scholar 

  19. Fonesca, C.M., Fleming, P.J.: Genetic Algorithms for multi objective Optimization: Formulation, Discussion and Generalization. In Forest, S. (ed.) Proceedings of the fifth Int’l conference on Genetic Algorithms. pp. 416–423, (1993)

  20. Horn, J., Nafpliotis, N., Goldberg, D.E.: A nitched Pareto genetic algorithm for multi objective optimization. IEEE World Congress Comput. Intell. 1, 82–87 (1994)

    Google Scholar 

  21. Srinivas, N., Deb, K.: Multi objective Optimization using Nondominated Sorting genetic algorithm. Evolutionary Computations. MIT Press, 2, (3):221–248 (1995)

  22. Bagchi, T.P.: Multi Objective Scheduling by Genetic Algorithms. Kluwer Academic. (1999)

Download references

Acknowledgement

I am very thankful to the reviewers for their valuable suggestions in the paper.

Author information

Author notes

  1. Ananya Chakraborty

    Present address: , Flat-214, Royal Palms Apt, L. B. Shastri Nagar, P.O. Vimanpura, Bangalore, 560017, Karnataka, India

Authors and Affiliations

  1. Department of Applied mathematics, Indian School of Mines, Dhanbad, Jharkhand, India

    Ananya Chakraborty & M. Chakraborty

Authors
  1. Ananya Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ananya Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chakraborty, A., Chakraborty, M. Multi criteria genetic algorithm for optimal blending of coal. OPSEARCH 49, 386–399 (2012). https://doi.org/10.1007/s12597-012-0089-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12597-012-0089-y

Keywords

Search

Navigation