Analysis and validation of a run-of-mine ore grinding mill circuit model for process control

2013

Powder Technology

Self Cite

The number of size classes in a cumulative rates model of a grinding mill circuit is reduced to determine the minimum number required to provide a reasonably accurate model of the circuit for process control. Each reduced size class set is used to create a non-linear cumulative rates model which is linearised to design a linear model predictive controller. The accuracy of a model is determined by the ability of the corresponding model predictive controller to control important process variables in the grinding mill circuit as represented by the full non-linear cumulative rates model.Results show that a model with 25 size classes that provides valuable information for plant design and scale-up, can be reduced to a model containing only a small number of size class sets and still be suitable for process control. Although as few as 3 size classes can be used to obtain a fairly accurate model for process control, the distribution of these 3 size classes influences the accuracy of the model. For a model to be useful for process control, the model should at least provide the directions in which the process variables change.

Section: Introductionmentioning

confidence: 99%

Reducing the number of size classes in a cumulative rates model used for process control of a grinding mill circuit

Roux

2013

Powder Technology

Self Cite

“…The reduced complexity nonlinear dynamic model of le Roux et al [30] can be used to describe the circuit shown in Fig. 1.…”

Section: Milling Circuit and Model Descriptionmentioning

confidence: 99%

“…A detailed description of the model can be found in le Roux et al [30]. The model divides the circuit into four modules: a feeder, a semi-autogenous mill with an end-discharge screen, a sump and a hydrocyclone.…”

Section: Milling Circuit and Model Descriptionmentioning

confidence: 99%

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

Roux

Padhi

Journal of Process Control

2014

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

“…A non-linear model was developed for the milling circuit as described in Coetzee et al (2010) andLe Roux et al (2012). The non-linear model was validated using real plant data as described in Le Roux et al (2012).…”

Section: Milling Circuit Modelmentioning

confidence: 99%

Demand side management of a run-of-mine ore milling circuit

Matthews

Control Engineering Practice

2013

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Increasing electricity costs coupled with lower prices for some metals such as platinum group metals, require a reevaluation of the operation of grinding processes. Demand side management (DSM) has received increasing attention in the field of industrial control as an opportunity to reduce operating costs. DSM through grinding mill power load shifting is presented in this paper using model predictive control and a real-time optimizer. Simulation results indicate that mill power load shifting can potentially achieve cost reductions of $9.90 /kg of unrefined product when applied to a run-of-mine (ROM) ore milling circuit processing platinum bearing ore. DSM is however still not economically feasible when there is a demand to continuously run the milling circuit at maximum throughput.