Reducing the number of size classes in a cumulative rates model used for process control of a grinding mill circuit

The number of size classes in a cumulative rates model of a grinding mill circuit is reduced to determine the minimum number required to provide a reasonably accurate model of the circuit for process control. Each reduced size class set is used to create a non-linear cumulative rates model which is linearised to design a linear model predictive controller. The accuracy of a model is determined by the ability of the corresponding model predictive controller to control important process variables in the grinding mill circuit as represented by the full non-linear cumulative rates model. Results show that a model with 25 size classes that provides valuable information for plant design and scale-up, can be reduced to a model containing only a small number of size class sets and still be suitable for process control. Although as few as 3 size classes can be used to obtain a fairly accurate model for process control, the distribution of these 3 size classes inuences the accuracy of the model. For a model to be useful for process control, the model should at least provide the directions in which the process variables change.

[1]  Ian K. Craig,et al.  Grinding Mill Circuits - A Survey of Control and Economic Concerns , 2008 .

[2]  Marappagounder Ramasamy,et al.  Control of ball mill grinding circuit using model predictive control scheme , 2005 .

[3]  Ian K. Craig,et al.  Fractional order and BICO disturbance observers for a run-of-mine ore milling circuit , 2012 .

[4]  José Luis Salazar,et al.  Dynamic modelling and simulation of semi-autogenous mills , 2009 .

[5]  W. J. Whiten A matrix theory of comminution machines , 1974 .

[6]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[7]  B. C. Flintoff,et al.  Cyclone modelling: a review of present technology , 1987 .

[8]  R. Amestica,et al.  A mechanistic state equation model for semiautogenous mills , 1996 .

[9]  Shihua Li,et al.  Application of model predictive control in ball mill grinding circuit , 2007 .

[10]  J. A. Herbst,et al.  Dynamic Simulators for Training Personnel in the Control of Grinding/Flotation Systems , 1985 .

[11]  Leonard G. Austin,et al.  The back-calculation of specific rates of breakage from continuous mill data , 1984 .

[12]  A. L. Hinde,et al.  The application of a simplified approach to modelling tumbling mills, stirred media mills and HPGR’s , 2009 .

[13]  Ian K. Craig,et al.  Specification framework for robust control of a run-of-mine ore milling circuit , 1995 .

[14]  L. G. Austin A mill power equation for SAG mills , 1990 .

[15]  Ian K. Craig,et al.  Model-plant mismatch detection and model update for a run-of-mine ore milling circuit under model predictive control ☆ , 2013 .

[16]  Shihua Li,et al.  Disturbance observer based multi-variable control of ball mill grinding circuits , 2009 .

[17]  P. Gottlieb,et al.  An analysis of SAG mill grinding and liberation tests , 1993 .

[18]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[19]  Eric C. Kerrigan,et al.  Robust Nonlinear Model Predictive Control of a Run-of-Mine Ore Milling Circuit , 2010, IEEE Transactions on Control Systems Technology.

[20]  S. Morrell,et al.  Mineral comminution circuits: their operation and optimisation , 1996 .

[21]  S. Morrell A new autogenous and semi-autogenous mill model for scale-up, design and optimisation , 2004 .

[22]  Qi Li,et al.  Constrained model predictive control in ball mill grinding process , 2008 .

[23]  Nina F. Thornhill,et al.  Inferential measurement of SAG mill parameters II: state estimation , 2002 .

[24]  Michael J. Nicol,et al.  The Extractive Metallurgy of Gold in South Africa , 1987 .

[25]  Ian K. Craig,et al.  Analysis and validation of a run-of-mine ore grinding mill circuit model for process control , 2013 .