Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

A Concept of Automatic Tuning of Longwall Scraper Conveyor Model

,

DOI: http://dx.doi.org/10.15439/2016F258

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 601–604 ()

Full text

Abstract. The modeling of machines and their operation modes is a key approach for optimization of their performance as well as for avoiding unwanted operational states which may lead to the occurrence of faults, and finally, to the breakdown. The developed model of a machine should be always parametrized, i.e. the certain number of parameters should be selected in the certain ranges. The most of the parameters can be selected based on engineering documentation and experts' knowledge, however, for some of them this knowledge cannot be directly acquired which leads to the parameter uncertainty. One of the approaches allowing selection of these uncertain parameters is a tuning procedure of a model. The paper deals with a concept of heuristic optimization method for automatic tuning of key parameters of longwall scraper conveyor model. In the first part of the paper, the evolutionary algorithm for tuning this model is proposed. In the case study, the merits and limitations of the evolutionary approach are analysed. The obtained results prove that the proposed approach of tuning of the considered model has high practical potential and it may be applied in real mining conditions.

References

  1. K. Cenacewicz and P. Przystałka, “Conveyor belt simulator with fault models,” Modelling in Engineering, vol. 24, no. 55, pp. 13–20, 2015, [in Polish].
  2. K. Cenacewicz and A. Katunin, “Modeling and simulation of longwall scraper conveyor considering operational faults,” Studia Geotechnica et Mechanica, vol. 38, no. 2, pp. 15–27, 2016. [Online]. Available: http://dx.doi.org/10.1515/sgem-2016-0015
  3. A. Mucherino and O. Seref, Advances in Modeling Agricultural Systems. Boston, MA: Springer US, 2009, ch. Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods, pp. 403–419. [Online]. Available: http://dx.doi.org/10.1007/978-0-387-75181-8_19
  4. P. M. Vasant and P. M. Vasant, Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, 1st ed. Hershey, PA, USA: IGI Global, 2013.
  5. J. Valadi and P. Siarry, Applications of Metaheuristics in Process Engi neering. Springer Publishing Company, Incorporated, 2014.
  6. M. Dolipski, E. Remiorz, and P. Sobota, “Determination of dynamic loads of sprocket drum teeth and seats by means of a mathematical model of the longwall conveyor,” Archives of Mining Sciences, vol. 57, no. 4, pp. 1101–1119, 2012. [Online]. Available: http://dx.doi.org/10.2478/v10267-012-0073-7
  7. M. Dolipski, Dynamics of chain conveyors. Gliwice: The Silesian University of Technology Press, 1997, [in Polish].
  8. A. M. Plamitzer, Electrical machines, 4th ed. Warsaw: WNT, 1976, [in Polish].