Control of ball mill grinding circuit using model predictive control scheme

Ramasamy, M.; Narayanan, S.; Rao, Ch. Durga

doi:10.1016/j.jprocont.2004.06.006

Cited by 122 publications

(52 citation statements)

References 6 publications

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“…The use of model-based controllers is preferred over traditional PID controllers for grinding mill circuits (Niemi et al, 1997;Pomerleau et al, 2000;Ramasamy et al, 2005). However, apart from the issue of computational time, the use of model-based controllers in industrial circuits are impeded by the lack of adequate plant measurements to estimate states and parameters for state feedback.…”

Section: State Estimationmentioning

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: State Estimationmentioning

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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“…Until now, there have been some attempts on solving this supervisory control problem. These begin with some model-based control and optimization methods, such as real-time optimization (RTO) [25], model predictive control (MPC) [2,[26][27][28] and adaptive decoupled control [29,30]. But, these methods are hard to be applied in practical MGPs, as accurate modeling is difficult to achieve or the established models do not accurately describe the actual dynamic processes.…”

Section: Control Situationmentioning

confidence: 99%

Hardware-in-the-loop simulation platform for supervisory control of mineral grinding process

et al. 2016

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“…Model predictive control, which is the advanced control method of choice in process industries as a whole, is becoming increasingly popular for milling circuit control (Muller and de Vaal, 2000;Ramasamy et al, 2005;Chen et al, 2007;Coetzee and Craig, 2007). Model predictive control has shown better performance in terms of setpoint tracking and the rejection of load disturbances (Freeman et al, 1994).…”

Section: Control Technologies Used In Milling Circuitsmentioning

confidence: 99%

Economic performance assessment of two ROM ore milling circuit controllers

Wei¹,

Craig²

2009

Minerals Engineering

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a b s t r a c tEconomic performance assessment of control systems is receiving increasing attention in both academia and industry. A relevant framework is proposed in Bauer and Craig (Bauer, M., Craig, I.K., 2008. Economic assessment of advanced process control -a survey and framework. Journal of Process Control 18 (1), 2-18) based on a comprehensive survey of the methods for economic analysis of advanced process control systems. The framework provides a formal procedure for the economic assessment of control systems. A practically motivated simulation study is used to cast the economic performance assessment of a nonlinear model predictive controller and three single-loop PID controllers for a run-of-mine milling circuit into this framework. Performance functions of the milling circuit, which dynamically relate the circuit process variables to monetary values, are developed based on a survey study on milling circuits and an in-depth literature study.

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Control of ball mill grinding circuit using model predictive control scheme

Cited by 122 publications

References 6 publications

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

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

Hardware-in-the-loop simulation platform for supervisory control of mineral grinding process

Economic performance assessment of two ROM ore milling circuit controllers

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