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Research Publications in journals:
Performance of some distributions to describe rock fragmentation data
Research Areas
  • Engineering
Ten functions, Weibull, Swebrec, Gilvarry, Grady and Lognormal, and their bi-component versions, are fitted to 448 sets of screened fragment size data from blasted and crushed rock. The ordinary least squares criterion has been used for the fits and two minimization techniques have been tested, in both cases running the problem repeatedly with different initial values of the unknown parameters in order to ensure a global minimum; 1000 runs per unknown has proved safe for this purpose. The errors in predicting sizes have been determined for each of the distribution functions. There is a distinct behavior of errors across the passing range, which has been divided in four zones, coarse (>80 %), central (80-20 %), fine (20-2 %) and very fine (<2 %). The representation of fragmentation data by some of the distributions can be made with good accuracy in the coarse and central zones, with moderate accuracy in the fine zone and with considerably poor accuracy in the very fines. As expected, bi-component distributions generally perform better than the single-components, though there are important differences among them. Extended Swebrec is consistently the best fitting distribution in all zones: maximum relative errors expected for it are less than 25 % in the coarse, 15 % in the central and 50 % in the fine zones. Bimodal Weibull, bimodal Gilvarry and bimodal Grady?s errors are statistically equivalent to extended Swebrec?s in the central and fine zones. In the very fine zone, relative errors have a high probability of being in excess of 100 %, with maximum expected values being several times that, even for the best fitting functions in this zone. Swebrec is by far the best single component function in all zones, with errors comparable to the best bi-components in the coarse and central.
International Journal of Rock Mechanics and Mining Sciences
Impact factor JCR
Impact info
Journal number
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6/30 en JCR - Engineering, Geological 7/23 en Mining and Mineral Processing
  • Autor: Jose Angel Sanchidrian Blanco (UPM)
  • Autor: Pablo Segarra Catasus (UPM)
  • Autor: Lina Maria Lopez Sanchez (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Ingeniería de Georrecursos y Modelización
  • Departamento: Ingeniería Química y Combustibles
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)