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Tytuł artykułu

Prediction of on-line froth depth measurement errors in industrial flotation columns: a promising tool for automatic control

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The pulp-froth interface position is important from a metallurgical point of view because it determines the relative importance of the cleaning and the collection zones. The pulp-froth interface position is measured based on variations of specific gravity, temperature or conductivity between the two zones to locate the pulp froth interface position. In this study, the pressure measurements are used to calculate the values of the froth layer height. These two meters are installed in the upper part of the column at 1.4 m and 2.4 m respectively, from the top of the column. Methods using pressure gauges are commonly used in industrial operations Even though their accuracy is limited (due to assumptions of uniformity of the pulp and froth density), and they always have some error. In the Sarcheshmeh copper industrial plant (Iran), a float was installed near the column with 2.5 m height that was calibrated to 5 cm intervals in order to determine the more exact forth height and compare it with the recorded froth height in control room. In this paper, an algorithm based on Kalman Filter is presented to predict on-line froth height errors using two pressure gauges. This research is based on the industrial real data collection for evaluating the performance of the presented algorithm. The quality of the obtained results was very satisfied. The RMS errors of prediction froth height errors was less than 0.025 m.
Słowa kluczowe
Rocznik
Strony
757--768
Opis fizyczny
Bibliogr. 9 poz., rys., tab.
Twórcy
autor
  • Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran
autor
  • Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran
Bibliografia
  • 1. BERGH L., YIANATOS J., ACUA C., 1995. Hierarchical Control Strategy for Flotation Columns. Minerals Engineering, 8(12), 1583-1591.
  • 2. DEL VILLAR R., GREGOIRE M., POMERLEAU A., 1999. Automatic Control of a Laboratory Flotation Column. Journal of Minerals Engineering, 12(3), 291–308.
  • 3. FINCH J. A., DOBBY G. S., 1990. Column Flotation. Pergamon Press.
  • 4. HYMA D., SALAMA A. I. A., 1993. Design and Implementation of a Process Control System for Column Flotation”, Vol.86, No.973, CIM Bulletin, 50–54.
  • 5. MOHANTY S., 2009. Artificial Neural Network Based System Identification and Model Predictive Control of a Flotation Column. Journal of Process Control, 19(6), 991–999.
  • 6. MOSAVI M. R., 2006. Comparing DGPS Corrections Prediction using Neural Network, Fuzzy Neural Network, and Kalman Filter. Journal of GPS Solution 10 (2), 97–103.
  • 7. MOSAVI M. R., MOHAMMADI K., REFAN M. H., 2002. Time Variance ARMA Processing on GPS Data to Improve Positioning Accuracy. The Asian GPS Conference, 125–128.
  • 8. PAL R., MASLIYAH J. 1991.Process Dynamics and Control of a Pilot Flotation Column”, Canadian Metallurgical Quaterly, Vol.30, No. 2, 87–94.
  • 9. SIMON D., 2006. Optimal State Estimation Kalman and Nonlinear Approaches. John Wiley.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-84c4b099-fae9-4205-bf40-888383fcff77
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