Model predictive control of semiautogenous mills (sag)

Salazar, José Luis; Valdés-González, Héctor; Vyhmesiter, Eduardo; Cubillos, Francisco

doi:10.1016/j.mineng.2014.03.029

Cited by 33 publications

(19 citation statements)

References 22 publications

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“…The ability of the fractional order controllers to control the grinding mill circuit is compared to the performance of an LMPC controller. LMPC is chosen as it can be considered to be the de-facto standard for advanced process control implementations in industry [51,52], and was successfully applied to control grinding mill circuits both in simulation and practice [1,2,3,53,54]. The aim of the LMPC controller can be described as: min…”

Section: Lmpc Controllermentioning

confidence: 99%

Control of a grinding mill circuit using fractional order controllers

Aguila‐Camacho

Roux

Duarte‐Mermoud

et al. 2017

Journal of Process Control

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This paper presents the design and application of fractional single-input-single-output (SISO) controllers to a grinding mill circuit, which is a multiple-input-multiple-output (MIMO) process. Two kinds of controllers are presented: fractional order proportional-integral (FOPI) controllers, and a combination of FOPI and fractional order model reference adaptive controllers (FOMRAC). The parameters of the controller are tuned using off-line particle swarm optimization. In the presence of disturbances and process noise, the SISO fractional controllers achieve similar or better performance compared to Linear Model Predictive Control (LMPC).

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Section: Lmpc Controllermentioning

confidence: 99%

Control of a grinding mill circuit using fractional order controllers

Aguila‐Camacho

Roux

Duarte‐Mermoud

et al. 2017

Journal of Process Control

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“…A SAG mill has the advantages of high operation rate, large output, and large crushing ratio, which makes SAG mills have the characteristics of short process time, low management cost, and suitability for large-scale production compared to other mills. Therefore, SAG mills are favored by large mines and are becoming more and more widely used in the mineral processing industry [1,2]. A SAG mill has low speed and heavy load, which cause the energy consumption of grinding operation to be very high.…”

Section: Introductionmentioning

confidence: 99%

Optimal Speed Control for a Semi-Autogenous Mill Based on Discrete Element Method

et al. 2020

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The rotation speed of a mill is an important factor related to its operation and grinding efficiency. Analysis and regulation of the optimal speed under different working conditions can effectively reduce energy loss, improve productivity, and extend the service life of the equipment. However, the relationship between the optimal speed and different operating parameters has not received much attention. In this study, the relationship between the optimal speed and particle size and number was investigated using discrete element method (DEM). An improved exponential approaching law sliding mode control method is proposed to track the optimal speed of the mill. Firstly, a simulation was carried out to investigate the relationship between the optimal speed and different operating parameters under cross-over testing. The model of the relationships between the optimal rotation speed and the size and number of particles was established based on the response surface method. An improved sliding mode control using exponential approaching law is proposed to track the optimal speed, and simulation results show it can improve the stability and speed of sliding mode control near the sliding surface.

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“…Considering the feed ore size distribution, Amestica et al presented a dynamic model by taking the masses of water, copper rocks and granules as state variables [7], [8]. In [9], the control model was developed with taking the delivery rate of copper rocks, the feed flow rate of water and the rotation speed as control inputs and considering the power, the filling rate and particle size reduction as output variables. However, there is no sensor to measure the particle size in the SAG mill and the influence of rotation speed on the crushing rate is not considered.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Model for a Class of Semi-Autogenous Mill Systems

2020

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In this paper, we develop a state space model of semi-autogenous (SAG) mill systems, which is of great significance to the mineral processing control. According to the practical enterprise production condition, the model is composed of two main parts. One is the ore circuit model. Compared with other models of ore circuit, the present model is simple and efficient. Another part is the SAG subsystem model where the state space model is utilized to depict the grinding mechanism. Furthermore, the grinding process of holdups are analyzed and presented with the common outputs such as power draw, pulp density and pulp flow. We consider the mass of rocks, granules and water as states and analyze the observability of the SAG subsystem model. For the ore circuit system and the SAG subsystem established respectively, unknown parameters of these models are identified by appropriate parameter estimation methods. Verified by the actual plant data, the result shows that the SAG mill system works well. INDEX TERMS SAG mill system, dynamic model, parameter identification.

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Model predictive control of semiautogenous mills (sag)

Cited by 33 publications

References 22 publications

Control of a grinding mill circuit using fractional order controllers

Control of a grinding mill circuit using fractional order controllers

Optimal Speed Control for a Semi-Autogenous Mill Based on Discrete Element Method

A Dynamic Model for a Class of Semi-Autogenous Mill Systems

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