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Accuracy of separation parameters resulting from errors of chemical analysis, experimental results and data approximation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accuracy of determination of different separation parameters and selectivity indicators depends on the error of chemical analysis of feed and separation products as well as experimental and approximation errors. In this paper different selectivity parameters were considered which formulae was based on the content of useful component in the feed, concentrate and tailing. It was shown that the impact of chemical analysis on the selectivity parameters was small and the error determined by means of partial derivative approach for a copper ore upgraded by flotation was negligible. Also experimental errors were found to be insignificant. The largest errors occurred for approximation of the upgrading data with inadequately selected selectivity indicators.
Słowa kluczowe
Rocznik
Strony
98--111
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30-059 Krakow
  • Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw
autor
  • AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30-059 Krakow
  • AGH University of Science and Technology, Al. Adama Mickiewicza 30, 30-059 Krakow
Bibliografia
  • BROZEK M., SUROWIAK A., 2010. Argument of separation at upgrading in the JIG, Archives of Mining Sciences, vol. 55(1), pp. 21-40.
  • DRZYMALA J., 2005. Evaluation and comparison of separation performance for varying feed composition and scattered separation results, International Journal of Mineral Processing, vol. 75, pp. 189-196.
  • DRZYMALA J., 2005-2008. Atlas of upgrading curves used in separation and mineral science and technology, Part I, Physicochemical Problems of Mineral Processing.
  • DRZYMALA J., AHMED H.A.M., 2005. Mathematical equations for approximation of separation results using the Fuerstenau upgrading curves, International Journal of Mineral Processing, Vol. 76, Issue: 1-2, pp. 55-65.
  • DRZYMALA J., LUSZCZKIEWICZ A., FOSZCZ D., 2010. Application of upgrading curves for evaluation of past, present and future performance of a separation plant, Mineral Processing and Extractive Metallurgy Review, vol. 31(3), pp. 165–175.
  • DUCHNOWSKA M., DRZYMALA J., 2011. Transformation of equation y = a(100 – x)/(a – x) for approximation of separation results plotted as Fuerstenau’s upgrading curve for application in other upgrading curves, Physicochemical Problems of Mineral Processing, vol. 47, pp. 123–130.
  • DUCHNOWSKA M., DRZYMALA, J., 2012. Self-similarity of upgrading parameters used for evaluation of separation results, International Journal of Mineral Processing, vol. 106–109, pp. 50–57.
  • FOSZCZ D., 2006. Estimation of parameters of regressive functions by means of classical method and bootstrap method, AGH Journal of Mining and Geoengineering, vol. 3(1), Uczelniane Wydawnictwa Naukowo-Dydaktyczne AGH, 67-78. [in Polish]
  • FOSZCZ D., NIEDOBA T., TUMIDAJSKI T., 2009. Chosen problems of balancing of copper ores beneficiation products, AGH Journal of Mining and Geoengineering, vol. 4, 71–80. [in Polish]
  • FOSZCZ D., NIEDOBA T., TUMIDAJSKI T., 2010. Analysis of possibilities of forecasting the results of Polish copper ores beneficiation with applied technology taken into account. AGH Journal of Mining and Geoengineering, vol. 4/1, 25-36. [in Polish]
  • FULLER W.A., 2006. Measurement Error Models, Wiley.
  • HAIR J.F., ANDERSON R.E., TATHAM R.L., BLACK W.C., 1995. Multivariate Data Analysis with Readings. Prentice Hall International, London.
  • HÄRDLE W., MÜLLER M., SPERLICH S., WERWATZ A., 2004. Nonparametric and Semiparametric Models, Springer.
  • JAMROZ D., NIEDOBA T., 2014. Application of Observational Tunnels Method to Select Set of Features Sufficient to Identify a Type of Coal, Physicochemical Problems of Mineral Processing, vol. 50(1), pp. 185-202.
  • JOHNSON R.A., WICHERN D.W., 2007. Applied multivariate statistical analysis. Prentice Hall, New York.
  • NEETHLING S.J., CILLIERS J.J., 2008. Predicting and correcting grade recovery curves. Theoretical aspects, Int. J. Miner. Process., 89, 17-22.
  • NIEDOBA T., 2013. Statistical analysis of the relationship between particle size and particle density of raw coal, Physicochemical Problems of Mineral Processing, vol. 49(1), 175-188.
  • NOWAK A., SUROWIAK A., 2013. Methodology of the efficiency factors of fine grained clayish suspensions separation in multileveled hydrocyclone systems, Archives of Mining Sciences, vol. 58(4), pp. 1209-1220.
  • VERA M.A., FRANZIDIS J_P., MANLAPIG E. V. 1999. An empirical equation for recovery - enrichment ratio curve (AREV model), Copper 99-Cobre 99 International Environment Conference, Volume II - Mineral Processing/Environment, Health and Safety, B.A. Hancock and M.R.L. Pon Eds, The Minerals, Metals & Materials Society, 69-82.
  • WACKERLY D., SCHEAFFER W., 2008. Mathematical Statistics with Applications (7 ed.). Belmont, CA, USA: Thomson Higher Education.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-acb3c965-8fca-4c51-8096-95e209e94062
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