Development of a Dynamic Population Balance Plant Simulator for Mineral Processing Circuits

Document Type: Research Paper

Authors

Mineral Processing Department, Tarbiat Modares University, Tehran, Iran

Abstract

Operational variables of a mineral processing circuit are subjected to different variations. Steady-state
simulation of processes provides an estimate of their ideal stable performance whereas their dynamic
simulation predicts the effects of the variations on the processes or their subsequent processes. In this
paper, a dynamic simulator containing some of the major equipment of mineral processing circuits
(i.e. ball mill, cone crusher, screen, hydrocyclone, mechanical flotation cell, tank leaching and
conveyor belt) was developed. The dynamic simulator of each mentioned unit was also developed
according to population balance models with the help of MATLAB/Simulink environment and was
verified against the data from the literature. Comminution and separation sections were linked using
empirical models which correlate the separation and extraction kinetics to particle size. Applying the
developed simulator, the dynamic behavior of a grinding-leaching circuit was analyzed and the results
showed that such simulations are required for both designing and controlling the circuits.

Keywords


[1] Aström, K.J., & Murray, R.M. (2010). Feedback systems: an introduction for scientists and engineers. Princeton university press.
[2] Hodouin, D. (2011). Methods for automatic control, observation, and optimization in mineral processing plants. Journal of Process Control, 21(2), 211225. doi: http://dx.doi.org/10.1016/j.jprocont.2010.10.016.
[3] Liu, Y., & Spencer, S. (2004). Dynamic simulation of grinding circuits. Minerals Engineering, 17(11–12), 1189-1198. doi: http://dx.doi.org/10.1016/j.mineng.2004.05.018.
[4] Hodouin, D., Dubé, Y., & Lanthier, R. (1988). Stochastic simulation of filtering and control strategies for grinding circuits. International Journal of Mineral Processing, 22(1–4), 261-274. doi: http://dx.doi.org/10.1016/0301-7516(88)90068-3.
[5] Asbjörnsson, G., Hulthén, E., & Evertsson, M. (2013). Modelling and simulation of dynamic crushing plant behavior with MATLAB/Simulink. Minerals Engineering, 43–44(0), 112-120. doi: http://dx.doi.org/10.1016/j.mineng.2012.09.006. [6] Hulthén, E., & Evertsson, C.M. (2009). Algorithm for dynamic cone crusher control. Minerals Engineering, 22(3), 296-303. doi: http://dx.doi.org/10.1016/j.mineng.2008.08.007.
[7] Asbjörnsson, G. (2013). Modelling and Simulation of Dynamic Behaviour in Crushing Plants. PhD thesis, Chalmers University of Technology.
[8] Sbâarbaro, D. & Villar, R. del. (2010). Advanced Control and Supervision of Mineral Processing Plants. Springer.
[9] Asbjörnsson, G., Hulthén, E., & Evertsson, M. (2012). Modelling and dynamic simulation of gradual performance deterioration of a crushing circuit – Including time dependence and wear. Minerals Engineering, 33(0), 13-19. doi: http://dx.doi.org/10.1016/j.mineng.2012.01.016.
[10] Hulthén, E. (2010). Real-time optimization of cone crushers. Chalmers University of Technology.
[11] Kianinia, Y., Khalesi, M.R., Khodadadi, A., & Froutan, A. (2012). Designing Dynamic Simulator of Grinding Circuit using SIMULINK. Iranian Journal of Mining Engineering, 7(17), 41-49.
[12] Khoshnam, F., Khalesi, M.R., & Zarei, M.J. (2013). Dynamic simulation of discharge of overflow mills based on mass balance equations in time and frequency domain. First International Conference on Mining, Mineral Processing, Metallurgical and Environmental Engineering, University of Zanjan.
[13] Zarei, M.J., Khalesi, M.R., & Khoshnam, F. (2013). Prediction of ball size variation effect on the output size distribution of mill by dynamic simulation of grinding. First International Conference on Mining, Mineral Processing, Metallurgical and Environmental Engineering, University of Zanjan.
[14] Hodouin, D., et al. (2000). Feedforward–feedback predictive control of a simulated flotation bank. Powder Technology, 108(2–3), 173-179. doi: http://dx.doi.org/10.1016/S0032-5910(99)00217-X.
[15] Pomerleau, A., et al. (2000). A survey of grinding circuit control methods: from decentralized PID controllers to multivariable predictive controllers. Powder Technology, 108(2–3), 103-115. doi: http://dx.doi.org/10.1016/S0032-5910(99)00207-7 

[16] Radhakrishnan, V.R. (1999). Model based supervisory control of a ball mill grinding circuit. Journal of Process Control, 9(3), 195-211. doi: http://dx.doi.org/10.1016/S0959-1524(98)00048-1.
[17] Yahyaee, M., & Banisi, S. (2004). How to control the level of column flotation cell in pilot plant of Sarcheshmeh. The conference of mining engineering of Iran, Tehran, Tarbiat Modares.
[18] Luyben, W. (1990). Process modeling, simulation, and control for chemical engineers. McGraw-Hill chemical engineering series.
[19] Rajamani, R.K., & Herbst, J.A. (1991). Optimal control of a ball mill grinding circuit—I. Grinding circuit modeling and dynamic simulation. Chemical Engineering Science, 46(3), 861-870. doi: http://dx.doi.org/10.1016/0009-2509(91)80193-3. [20] de Andrade Lima, L.R., & Hodouin, D.(2003). Online Optimization of a Gold Extraction Process. in Control and Automation, ICCA'03. Proceedings. 4th International Conference on. 2003: IEEE.
[21] Adel, G.T., & Luttrell, G.H. (1999). An advanced control system for fine coal flotation. US Department of energy-contract DE-AC22-95PC95150.
[22] Whiten, W. (1972). The simulation of crushing plants with models developed using multiple spline regression. Journal of the Soutf African Institute of Mining And Metallurgy, 257-264.
[23] Karra, V. (1979). Development of a model for predicting the screening performance of a vibrating screen. Cim Bulletin, 72 167-171.
[24] Darling, P. (2011). SME Mining engineering handbook. Vol. 1. SME.
[25] King, R.P. (2001). Modeling and simulation of mineral processing systems. Access Online via Elsevier.
[26] Khalesi, M.R., et al. (2009). A liberation model for the integrated simulation of grinding and leaching of gold ore. in World Gold Conference 2009., The Southern African Institute of Mining and Metallurgy: Johannesburg, South Africa, 61-73.
[27] Khalesi, M.R., et al. (2011). Modelling of the Gold Content within the Size Intervals of a Grinding Mill Product. in World Gold Conference 2011. Montreal, Canada.
[28] de Andrade Lima, L.R., & Hodouin, D. (2005). A lumped kinetic model for gold ore cyanidation. Hydrometallurgy, 79(3), 121-137. doi: http://dx.doi.org/10.1016/j.hydromet.2005.06.001.
[29] Conradie, A., & Aldrich, C. (2001). Neurocontrol of a ball mill grinding circuit using evolutionary reinforcement learning. Minerals engineering, 14(10), 1277-1294. doi: http://dx.doi.org/10.1016/S0892-6875(01)00144-3
[30] Itävuo, P., et al., (2013). Dynamic modeling and simulation of cone crushing circuits. Minerals Engineering, 43–44(0), 29-35. doi: http://dx.doi.org/10.1016/j.mineng.2012.07.019.