Simulation of Choghart AG mill circuit using JKSimMet software and investigating the possibility of converting to SAG mill

Document Type : Research Article

Authors

1 Dept. of Mining and Metallurgy, Yazd University

2 Mining Technologies Research Center

3 Dept. of Mining, Azad University, Bafgh Branch

10.29252/anm.8.16.71

Abstract

Summary
The variation of feed characteristics of the Choghart Iron Processing Plant has resulted in the reduction of AG mill efficiency. In this research, the possibility of converting the Choghart AG mill to SAG mill to stabilize the mill power draw and improve the grinding process was investigated. The JKSimMet software was used to simulate the circuit and predict the effect of charging steel balls to the AG mill. The results of grinding tests on the ore samples were used to simulate the circuit. The product size distribution predicted by the software was very close to the actual product size of the AG mill. The simulation of the circuit, using different tonnage of 100 mm steel balls showed that the mill throughput could increase by around 6 percent with a slight increase in the mill product size. The actual results of adding the steel ball to the plant were in agreement with the simulation results.
 
Introduction
The throughput of Choghart AG mill circuit was lower than the expected level for grinding the oxidized (or high Fe/FeO ratio) ores. Converting the AG mill to SAG mill to increase the grinding of this type of ore was the investigated option. The circuit was simulated by JKSimMet software and then adding of steel ball to the system was evaluated. The results were validated using the plant-scale studies.
 
Methodology and Approaches
Samples from the feed and product streams were collected, the relevant grinding tests were conducted and the selection and breakage functions of the ore were determined. The ore breakage test results and the specification of the feed material and AG mill and vibrating screen were used as input data to the JKSimMet software. Then charging different tonnage of the steel balls to the AG mill was evaluated. Plant-scale experiments were also conducted.
 
Results and Conclusions
Based on the impact and abrasion tests of the feed samples this ore was considered as a medium hard type ore. The simulation result for the mill product size distribution was very close to the actual data of the plant. Therefore, this software could be used to predict other changes in the circuit. The simulation results showed that by adding 5 and 10 percent (volume) of 100 mm steel balls to the Choghart AG mill its throughput could be increased by around 3 and 7 percent, respectively. The plant scale experiments with the same type of steel ball were in agreement with the simulation results. As it was expected the mill product size increased slightly

Keywords

Main Subjects

References

[1]           Schwarz, S. and Richardson, J. (2013). Modelling and simulation of mineral processing circuits using JKSimMet and JKSimFloat. In: 2013 SME Annual Meeting & Exhibit (SME 2013)
[2]        Banisi, S. (2008). Course notes of crushing Engineering. Shahid bahonar university, Kerman, Iran (In Persian).
[3]        Parandeh, L. Dehghani, A. Khosravi, M. (2010) the comparison of the up-scaled laboratory selection function and the industrial-scale selection function. Case study Se-Chahun Ball Mill circuit. In third Conference of IRSME, bafgh (In Persian).
[4]        Bueno, M. D. P. (2013). Development of a multi-component model structure for autogenous and semi-autogenous mills.
[5]        Bueno, M. P., Kojovic, T., Powell, M. S., & Shi, F. (2013). Multi-component AG/SAG mill model. Minerals Engineering, 43, 12-21.
[6]        Mitchell, D. (2015). Improved relationships for discharge in SAG/AG mills.
[7]        Morrell, S., & Morrison, R. D. (1996). AG and SAG mill circuit selection and design by simulation. In International Conference on Autogenous and Semiautogenous Grinding Technology (Vol. 2, pp. 769-790).
[8]        JKSimMet 2001, Steady State Mineral Processing Simulator Software help file, Version 5.1, JKRMC Commercial Division, Australia
[9]        Napier-Munn, T. J., Morrell, S., Morrison, R. D., & Kojovic, T. (1996). Mineral comminution circuits: their operation and optimisation (Vol. 2). Julius Kruttschnitt Mineral Research Centre, University of Queensland.
[10]      McKen, A., & Williams, S. (2006). An overview of the small-scale tests available to characterise ore grindability. Proceedings international autogenous and semiautogenous grinding technology, 4, 315-330.
[11]      King, R. P. (2012). Modeling and simulation of mineral processing systems. Elsevier.
[12]      Nematollahi, H. (1999). Mineral Processing (Vol. 1). Tehran University (In Persian).
[13]      Rosario, P. P. (2010). Comminution circuit design and simulation for the development of a novel high pressure grinding roll circuit (Doctoral dissertation, University of British Columbia).
[14]      Starkey, J., & Meadows, D. (2007, January). Comparison of ore hardness measurements for grinding mill design for the Tenke project. In CMP Conference Proceedings (pp. 19-31).
[15]      Dehghani Ahmadabadi, M.(2008). Geological remodeling Choghart iron ore mine based on drilling information with geostatistic method. MSc thesis. Yazd University (In Persian).
[16]      Hassani, S & Dehghani, A & Khosravi-Rad, M. (2010). Simulation of Choghart AG mill circuit using MODSIM software. IRJME, Vol 5, No 9, pp.13-25 (In Persian).
[17]      Morrell, S. (2004). A new autogenous and semi-autogenous mill model for scale-up, design and optimisation. Minerals Engineering, 17(3), 437-445.
[18]      Deniz, V. (2011). A new size distribution model by t-family curves for comminution of limestones in an impact crusher. Advanced Powder Technology, 22(6), 761-765.
[19]      Hosseinzadeh, H., & Ergun, L. (2012). ‘Determination of breakage distribution function of fine chromite ores with bed breakage method. In XIII International Mineral Processing Symposium, Bodrum, Turkey. (In Persian).
Journal of Analytical and Numerical Methods in Mining Engineering
Volume 8, Issue 16
October 2018
Pages 71-85

Files

History

  • Receive Date: 26 January 2017
  • Revise Date: 17 November 2018
  • Accept Date: 05 June 2018

Share

How to cite

Statistics