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Journal of the Southern African Institute of Mining and Metallurgy

versão On-line ISSN 2411-9717

Resumo

ROBINSON, G.K.; SINNOTT, M.D.  e  CLEARY, P.W.. Summary of results of ACARP project on cross-belt cutters. J. S. Afr. Inst. Min. Metall. [online]. 2010, vol.110, n.6, pp. 331-338. ISSN 2411-9717.

A project was funded by the Australian coal industry to investigate the mechanisms that might lead to sample bias when using crossbelt cutters, in order to help coal industry personnel to make better decisions about the purchase, maintenance, and operation. It concentrated on DEM modelling of skew cutters. These are set at an angle to the belt with the intention of minimizing disturbance to the non-sampled material. Two bias mechanisms are likely to cause bias for cross-belt cutters. Waves of material are bulldozed off the belt by the upstream side of the body of square cutters and material is thrown by the leading edges of cutter blades for all types of cross-belt cutters. These mechanisms cause some parts of the load of material on a belt to be over-represented. The effects of these mechanisms cannot be made to be negligible, so cross-belt samplers cannot be trusted to produce unbiased samples, especially for segregated streams of material. However, it is possible to give a bound on the maximum likely bias. The grades of two portions of the stream can be estimated by stopping the belt and shovelling off 1/3 of the cross-section of the load on the belt into a container, concentrating on the final side of the belt and the top of the load. The remaining material should be put into another container and the difference in grade determined. The maximum likely bias is typically about 10% of this difference. For a cross-belt cutter, having an extraction ratio near to 100% is not a reliable indication that the cutter has little or no bias. Some bias mechanisms affecting cross-belt sample cutters make sample mass too high and some make it too low, so an extraction ratio near 100% can occur if two bias mechanisms are both active.

Palavras-chave : sampling; DEM simulation; sample bias; accuracy; precision.

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