Inferential measurement of SAG mill parameters V: MPC simulation

Apelt, T.A.; Thornhill, Nina F.

doi:10.1016/j.mineng.2009.01.011

Cited by 10 publications

(12 citation statements)

References 8 publications

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“…Substituting the common formula of circular motion v = π rn 30 into (8), the relation between the radical position and the charge shoulder angle is given by…”

Section: Power Drawmentioning

confidence: 99%

“…Apelt et al made enormous contributions to the model and control for the SAG mill [3]- [6]. 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.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Substituting the common formula of circular motion v = π rn 30 into (8), the relation between the radical position and the charge shoulder angle is given by…”

Section: Power Drawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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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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“…The economic performance of PID control compared to nonlinear MPC when applied to a grinding mill circuit was evaluated by Wei and Craig [9] and results showed that nonlinear MPC (NMPC) can improve performance with respect to recovered mineral value in downstream flotation circuits. Further improvements to overall MPC performance can be achieved by incorporating peripheral control tools such as inferential measurements, disturbance observers or model-plant mismatch detection [10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [3], Niemi et al [5], Pomerleau et al [6], Ramasamy et al [7], Remes et al [8], Apelt and Thornhill [11], Yang et al [12],Chen et al [14]. Because of the highly nonlinear nature of a grinding process, the use of NMPC with fundamental nonlinear models is more desireable.…”

Section: Introductionmentioning

confidence: 99%

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

Roux

Padhi

Craig

2014

Journal of Process Control

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The recently developed reference-command tracking version of model predic- Email address: derik.leroux@up.ac.za (Johan D. le Roux) 1 This work was carried out while the author was a visiting professor at the University of Pretoria, South Africa. February 17, 2015 in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. Preprint submitted to Journal of Process Control

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Inferential measurement of SAG mill parameters V: MPC simulation

Abstract: This paper discusses a case study application of inferential measurement models for semiautogenous grinding (SAG)

Cited by 10 publications

References 8 publications

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

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

Control of a grinding mill circuit using fractional order controllers

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

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