Application of model predictive control in ball mill grinding circuit

Chen, Xisong; Zhai, Junyong; Li, Shihua; Li, Qi

doi:10.1016/j.mineng.2007.04.007

Cited by 74 publications

(46 citation statements)

References 11 publications

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“…Significant improvement in product quality, throughput and power consumption is possible through multivariable control techniques. This is illustrated by the industrial implementation of a multivariable controller on a ROM ore grinding circuit documented in Hulbert et al (1990) and Craig et al (1992b), the robust controller applied to an industrial semi-autogenous (SAG) mill in MacLeod (1995, 1996), and the linear model predictive control for an industrial ball mill circuit in Chen et al (2007).…”

Section: Control Of Grinding Mill Circuitsmentioning

confidence: 99%

Throughput and product quality control for a grinding mill circuit using non-linear MPC

Roux

Olivier

Naidoo

et al. 2016

Journal of Process Control

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A non-linear model-based control architecture for a single-stage grinding mill circuit closed with a hydrocyclone is proposed. The control architecture aims to achieve independent control of circuit throughput and product quality, and consists of a non-linear model predictive controller for grinding mill circuit control, and a dynamic inversion controller to control the fast sump dynamics. A particle filter is used to estimate the mill states, and an algebraic routine is used to estimate the sump states. The observers make use of real-time continuous measurements commonly available on industrial plants. Simulation results show that control objectives can be achieved by the controller despite the presence of measurement noise and disturbances.

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Section: Control Of Grinding Mill Circuitsmentioning

confidence: 99%

Throughput and product quality control for a grinding mill circuit using non-linear MPC

Roux

Olivier

Naidoo

et al. 2016

Journal of Process Control

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“…A typical structure of a closed-loop circuit for wet grinding consists of a ball mill, sump and classifier [10,13,33,39] and it is schematically shown in Fig. 4.…”

Section: Iii1 Process Variables and Characteristicsmentioning

confidence: 99%

“…Subsequently, control strategies utilizing the DMC algorithm have become a powerful tool for process control in various ball mill grinding circuits [8,10,39]. In [8] an implementation of the DMC algorithm is presented as it is shown in Fig.…”

Section: Iii3 Multivariable Controlmentioning

confidence: 99%

Grinding in Ball Mills: Modeling and Process Control

Monov¹,

Sokolov²,

Stoenchev³

2012

Cybernetics and Information Technologies

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“…The interactions between variables in primary and secondary milling circuits, real and simulated, may be characterised by transfer functions (Radhakrishnan, 1999;Freeman et al, 2000;Ivezič and Petrovič, 2003;Ramasamy et al, 2005;Chen et al, 2007;Apelt, 2007). The plant data and inferential measurement model results, discussed in the previous paper (Apelt and Thornhill, In Press), were analysed to determine estimates for the interactions between key variables.…”

Section: Model Predictive Controller Simulationmentioning

confidence: 99%