Investigation of laboratory conditions effect on prediction accuracy of size distribution of industrial ball mill discharge by using a perfect mixing model. A case study: Ozdogu copper-molybdenum plant
 
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1
Urmia University of Technology (UUT) Department of Mining Engineering
 
2
Hacettepe University Mining Engineering Department
 
3
Department of Mining Engineering, Urmia University, Urmia, Iran.
 
 
Publication date: 2017-05-22
 
 
Corresponding author
Hojjat H. Gharehgheshlagh   

Urmia University of Technology (UUT) Department of Mining Engineering, Band Road - Urmia – Iran, 555 Urmia, Iran
 
 
Physicochem. Probl. Miner. Process. 2017;53(2):1175-1187
 
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ABSTRACT
In this study, the perfect mixing model was used to investigate its accuracy level, under different laboratory conditions, in predicting the particle size distribution of industrial ball mills discharges. For this purpose, data sets of two laboratory ball mills with eight different compositions of balls and two industrial mills of a copper processing plant for seven different tonnages, which totally included 56 simulation operations, were used. For simulation, the necessary data were obtained through performing the breakage distribution function and kinetic grinding tests using laboratory mills. The results were used to determine the first order grinding kinetics and normalized breakage rate parameters. For the industrial scale, the simulation process was carried out using data, perfect mixing model equations and JKSimMet software. The results showed that the operating conditions of the laboratory mills were quite affected by the predictive power of the desired model. Comparing the measured and simulated values of P80, it is clear that 2 minutes of first order grinding using the Bond laboratory ball mill with standard operating conditions and single size ball load of 20 mm provided the best prediction with trivial errors, less than 10%, for all seven tonnages of the industrial mills. The results of this study together with more investigations on different plants can be helpful in optimization, simulation and scale-up procedures of ball mills.
 
REFERENCES (20)
1.
AUSTIN, L. G., KLIMPEL, R. R., LUCKIE, P. T., 1984, Process Engineering of Size Reduction: Ball Milling, American Institute of Mining, Metallurgical and Petroleum Engineers Inc, New York, 561.
 
2.
AUSTIN, L.G., BAGGA, P., CELIK, M., 1981, Breakage properties of some materials in a laboratory ball mill, Powder Technol. 28, 235–240.
 
3.
AUSTIN, L.G., LUCKIE, P.T., 1972, Methods for determination of breakage distribution parameters, Powder Technology, 5, 215-222.
 
4.
BOND, F. C., 1961, Crushing and Grinding Calculations; Part I, British Chemical Engineering, Volume 6, No. 6, 378-385.
 
5.
BROADBENT, S. R., CALLCOTT, T. G., 1956, A Matrix Analysis of Processes Involving Particle Assemblies, Phil. Trans. R. S. Soc. London., Ser., A249, 99-123.
 
6.
EPSTEIN, B., 1947, The Material Description of Certain Breakage Mechanisms Leading to the Logorithmic-Normal Distribution, J. Franklin Inst., 244, 471–477.
 
7.
ERDEM, A.S., ERGUN, S.L., 2009, The effect of ball size on breakage rate parameter in a pilot scale ball mill, Minerals Engineering, 22, 660–664.
 
8.
FUERSTENAU, M., KENNETH, N., 2003, Principles of Mineral Processing, SME, Chapter 3, p 61.
 
9.
GARDNER, R.P., AUSTIN L.G., 1962, A Chemical Engineering Treatment of Batch Grinding, In: H.Rumpf and D. Behrens (Editors), Proceedings, 1st European Symp. Zerkeinern. Verlag Chemie, Weinheim, 217-247.
 
10.
HERBST, J. A., FUERSTENAU, D. W., 1980, Scale-Up Procedure for Continuous Grinding Mill Design Using Population Balance Models, IJMP, 7, 1-31.
 
11.
KAVETSKY, A., WHITEN, W.J., 1982, Scale-up relations for industrial ball mill Processing, Australia’s Inst. Min. Metall., 282, 47-55.
 
12.
KELSALL D.F., REID KJ., 1969, Symposium on size reduction, Sydney University Chem. Engineering Association.
 
13.
LYNCH, A. J., 1977, Mineral Crushing and Grinding Circuits, , their simulation, optimization, design and control, Elsevier Scientific Publication Co., Amsterdam., pp 340.
 
14.
MAN, Y. T., 2000, A Model-Based Procedure for Scale-Up of Wet, Overflow Ball Mills, JKMRC Department of Mining, Minerals and Materials Engineering, Degree of Doctor of Philosophy, The University of Queensland.
 
15.
MORRELL, S., 1992. The simulation of autogenous and semi-autogenous milling circuits. In: Komar Kawatra, S. (Ed.), Comminution: Theory and Practice, pp. 369–380.
 
16.
MORRELL, S., MAN, Y.T., 1997, Using Modelling and Simulation for the Design of Full Scale Ball Mill Circuits, Minerals Engineering, Volume 10, No. 12, 1311-1327.
 
17.
NAPIER-MUNN, T.J., MORRELL, S., MORRISON, R.D., KOJOVIC, T., 1996, Mineral Comminution Circuits: Their Operation and Optimization, JKMRC, Queensland, Australia.
 
18.
WHITEN W.J., 1971, Proceeding, Symposium on Automatic Control Systems Mineral Processing Plants, AusIMM, Southern Queensland branch, 129-148.
 
19.
WHITEN, W.J., 1974, A matrix theory of comminution machines, Chem. Eng. Sci. No. 29, 585-599.
 
20.
WHITEN, W.J., KAVETSKY, A., 1984, Studies on Scale-Up of Ball Mills, Minerals and Metallurgical Processing, 23-28.
 
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