Fuzzy Evaluation of stream sample reability
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Fuzzy Evaluation of stream sample reability

DisciplinaProcessamento de Minerais I206 materiais2.052 seguidores
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a new method to evaluate the sample reliability in a
mineral-processing plant. The sample reliability is assumed to be influ-
enced by the cutter geometry, speed, layout, and path, and its degree is
determined employing linguistic terms such as very reliable, reliable,
adequate, doubtful, and very doubtful. The fuzzy evaluation method is
applied to a simulated sampling situation. From the previous develop-
ment, fuzzy logic appears to be a strong tool for estimating the sample
reliability. This estimation is the basis for decision-making concerning
sampling strategy and data reconciliation. Fuzzy evaluation of sample
reliability permits the use of sampling knowledge dealing with correct-
ness requirements. However, the conventional crisp modeling does not
take advantage of it. Consequently, fuzzy logic improves the understand-
ing of the sampling process, which results in efficient control of the plant
Cipriano, A. and Ramos, M., 1994, \u2018\u2018Fuzzy model based control for a mineral
flotation plant,\u2019\u2019 Proceedings of IECON \u201894, Vol. 2, New York: IEEE,
pp. 1375\u20131380.
Gy, P., 1988, He´te´roge´ne´ite´, E´chantillonnage, Homoge´ne´isation, Paris: Masson.
Gy, P., 1992, Sampling of Heterogeneous and Dynamic Material Systems, Amster-
dam: Elsevier.
Hall, M. B. and Harris, C. A., 1993, \u2018\u2018Fuzzy logic expert system for iron ore pro-
cessing,\u2019\u2019 Proceedings of IEEE Industry Application Society Annual Meeting,
Vol. 3, New York: IEEE, pp. 2190\u20132199.
Harris, C. A. and Meech, J. A., 1987, \u2018\u2018Fuzzy logic: A potential control technique
for mineral processing,\u2019\u2019 CIM Bulletin, 80(905), pp. 51\u201359.
Hodouin, D. and Ketata, C., 1994, \u2018\u2018Variance of average stream compositions
obtained by automatic incremental sampling,\u2019\u2019 International Journal of
Mineral Processing, 40, pp. 199\u2013223.
Ketata, C., 1991, Influence de la Structure de Variation des Signaux dans les Usines
Mine´ralurgiques sur les Erreurs d\u2019E´chantillonnage et la Pre´cision des Bilans de
Matie`re, M.Sc. Thesis, Laval University, Ste-Foy, Quebec, Canada.
Ketata, C., 1998, Knowledge-Assisted Stochastic Evaluation of Sampling Errors
in Mineral Processing Streams, Ph.D. Dissertation, Dalhousie University
(DalTech), Halifax, Nova Scotia, Canada.
Ketata, C. and Rockwell, M. C., 2001, \u2018\u2018Stochastic evaluation of sampling errors
in mineral processing streams,\u2019\u2019 CIM Bulletin, 94(1056), pp. 88\u201391.
Pitard, F., 1993, Pierre Gy\u2019s Sampling Theory and Sampling Practice: Heterogeneity,
Sampling Correctness, and Statistical Process Control, 2nd Ed., Boca Raton,
Santos, R. R., Meech, J. A., and Ramos, L. T. S., 1995, \u2018\u2018Thickener operations at
carajas using a fuzzy logic controller,\u2019\u2019 IEEE International Conference on Sys-
tems, Man and Cybernetics, Intelligent Systems for the 21st Century, Vol. 2,
New York: IEEE, pp. 1636\u20131639.
Tanaka, Y., 1993, \u2018\u2018An overview of fuzzy logic,\u2019\u2019 Proceedings of WESCON \u201893,
New York: IEEE, pp. 446\u2013450.
Zadeh, L. A., 1965, \u2018\u2018Fuzzy sets.\u2019\u2019 Information and Control, 8, pp. 338\u2013353.
Zadeh, L. A., 1984, \u2018\u2018Making computers think like people.\u2019\u2019 IEEE Spetrum,
August, pp. 26\u201332.
Zadeh, L. A., 1989, \u2018\u2018Knowledge representation in fuzzy logic.\u2019\u2019 IEEE Transac-
tions on Knowledge and Data Engineering, 1(1), pp. 89\u2013100.