The development of dynamic models for a dense medium separation circuit in coal beneficiation

Abstract Often the most difficult step in establishing a control system is the development of a suitable dynamic process model. As such a model is not available elsewhere, a first principle dynamic mathematical model was developed for a coal dense medium separation circuit. Each unit operation was modelled individually and then integrated together to form a complete non-linear state–space model for the circuit. This model was used to simulate the process and it was validated using real process data derived from a plant experiment. When developing models from first principles, it is necessary to estimate the model parameters. These parameters, specifically for non-linear state–space relationships, require a unique solution. A parameter identifiability method was used to show that the non-linear dynamic models developed have unique parameters for a specific set of input–output data.

[1]  Brendan William Botha.  Management of the mineral resource risk associated with near-density material in the beneficiation plant at Leeuwpan Coal Mine , 2009 .

[2]  G. J. de Korte.  Comments on the Use of Tracers to Test Dense-Medium Plant Efficiency , 2003 .

[3]  A. Patwardhan,et al.  In-Plant Evaluation of Dense Medium Process Performances , 2006 .

[4]  G. J. de Korte.  The Influence of Near-Dense Material on the Separation Efficiency of Dense-Medium Processes , 2008 .

[5]  Ian K. Craig,et al.  Economic assessment of advanced process control – A survey and framework , 2008 .

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

[7]  M. S. Brennan,et al.  Multiphase CFD simulations of dense medium and classifying hydrocyclones , 2003 .

[8]  A. J. Lynch,et al.  Mineral Crushing and Grinding Circuits: Their Simulation, Optimisation, Design and Control , 1977 .

[9]  Byron S. Gottfried,et al.  Computer simulation of coal preparation plants , 1978 .

[10]  Wu Chen,et al.  Evaluation of hydrocyclone models for practical applications , 2000 .

[11]  Geoff Barton,et al.  Process control: Designing processes and control systems for dynamic performance , 1996 .

[12]  Béla G. Lipták,et al.  Instrument Engineers Handbook Process Measurement And Analysis , 2005 .

[13]  Jose A. Romagnoli,et al.  Robust control of a SAG mill , 2002 .

[14]  editor-in-chief Bela G. Liptak.  Instrument Engineers' Handbook Process Control , 2010 .

[15]  Mohloana K. Magwai,et al.  The effect of cyclone geometry and operating conditions on spigot capacity of dense medium cyclones , 2008 .

[16]  Ian K. Craig,et al.  Evaluation of advanced industrial control projects: a framework for determining economic benefits , 2000 .

[17]  Rahul Konnur,et al.  Modeling the size–density partition surface of dense-medium separators , 2003 .

[18]  Cristóbal Cortés,et al.  Modeling the gas and particle flow inside cyclone separators , 2007 .

[19]  Timothy J. Napier-Munn,et al.  A mathematical model of recovery of dense medium magnetics in the wet drum magnetic separator , 2003 .

[20]  Janusz S. Laskowski,et al.  Effect of dense medium properties on the separation performance of a dense medium cyclone , 1994 .

[21]  A Scott Iain,et al.  METALLURGICAL EVALUATION OF IRON ORE DRUM SEPARATORS USING DENSITY TRACERS. , 1987 .

[22]  Timothy J. Napier-Munn,et al.  Modelling and simulating dense medium separation processes — A progress report , 1991 .

[23]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[24]  C. J. Clarkson.  A Model of Dense Medium Cyclones , 1989 .

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

[26]  B. V. Rao.  Weibull partition surface representation for gravity concentrators , 2004 .

[27]  R. P. King.  Practical Optimization Strategies for Coal-washing Plants , 1999 .

[28]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[29]  Xiaohua Xia,et al.  Identifiability of nonlinear systems with application to HIV/AIDS models , 2003, IEEE Trans. Autom. Control..

[30]  Deshan Gu,et al.  Rapid analysis of coal properties , 2005, IEEE Instrumentation & Measurement Magazine.

[31]  Béla G. Lipták,et al.  Instrument engineers' handbook , 2012 .

[32]  A. K. Majumder,et al.  A New Approach to Evaluate the Performance of Gravity-Based Coal Washing Equipment , 2004 .

[33]  Amalendu Sinha,et al.  Coal-Fine Beneficiation Studies of a Bench-Scale Water-Only Cyclone Using Artificial Neural Network , 2008 .

[34]  R. P. King,et al.  Cleaning of fine coals by dense-medium hydrocyclone , 1984 .

[35]  P.V.T Rao,et al.  Effect of increase in feed inlet pressure on feed rate of dense media cyclone , 2003 .

[36]  Peter C. Hayes,et al.  Process Principles in Minerals and Materials Production , 2003 .

[37]  R. P. King,et al.  Performance of a Dense-Medium Cyclone When Beneficiating Fine Coal , 1988 .

[38]  Graham C. Goodwin,et al.  Optimization opportunities in mining, metal and mineral processing , 2008, Annu. Rev. Control..

[39]  M. W. Mikhail,et al.  Automatic In-line Ash Monitor for Coal Slurries , 1980 .

[40]  Fuel Research Institute of South Africa , Nature.

[41]  Timothy J. Napier-Munn,et al.  A dense medium cyclone model based on the pivot phenomenon , 1988 .

[42]  George Stephanopoulos,et al.  Chemical Process Control: An Introduction to Theory and Practice , 1983 .

[43]  Daniel J. SUASNABAR.  A CFD MODEL FOR DENSE MEDIUM CYCLONES , 1999 .

[44]  George Lindfield,et al.  Numerical Methods Using MATLAB , 1998 .

[45]  Letsane Paul Rathaba.  Model Fitting for Electric Arc Furnace Refining , 2006 .