Laurentz Eugene Olivier

Parameter Mismatch Detection in a Run-Of-Mine Ore Milling Circuit under Model Predictive Control

Abstract The performance of a model predictive controller depends on the quality of the plant model that is available. Often parameters in a Run-of-Mine (ROM) ore milling circuit are uncertain and inaccurate parameter estimation leads to a mismatch between the model and the actual plant. Although model-plant mismatch is inevitable, timely detection of significant mismatch is desirable in order to prevent deteriorated control performance. This paper presents a simulation study of the detection of mismatch in the parameters of a ROM ore milling circuit model using a partial correlation analysis approach. The location of the mismatch in the MIMO model matrix is correctly detected.

Fractional order disturbance observer for a run-of-mine ore milling circuit

Grinding mill circuits are hard to control with high performance due to poor modeling, large external disturbances, and uncertainties from internal couplings. This paper proposes a novel fractional order disturbance observer (FO-DOB) for a run-of-mine (ROM) ore milling circuit. A fractional order low pass filter (Q-filer) is used in the DOB to offer an additional degree of freedom in tuning for set-point tracking performance and disturbance rejection performance. A 3×3 linear time invariant MIMO plant model is used for evaluation of the performance gained over other commonly used controllers such as PID and MPC. The simulation results show that the FO-DOB scheme offers promising potential in real world ROM milling circuit implementations.

Lights-out process control — Analysis and framework

Process control theory has developed to the point where advanced controllers are able to perform optimization and even planning functions on complex, possibly nonlinear, processes. Advances in digital technologies have enabled the industrial implementation of many of these automation strategies, resulting in the process industry achieving all-time highs in production with less need for human intervention. Will increased automation and control lead to fully automated, lights-out, process plants? The concept of lights-out process control is explored in this work, and a framework is provided for its implementation.

A survey on the degree of automation in the mineral processing industry

A survey was conducted regarding the degree of automation in the mineral processing industry. The aim of the survey was to gauge the current status of automation in the industry, and to identify opportunities for improved operation through process control. Key results indicate that there is still a large scope for optimizing control in the industry, especially regarding proper disturbance rejection, reacting to significant changes in operating point, and control in the presence of faults.

Model-Plant Mismatch Expression for Classically Controlled Systems

Abstract This paper presents a closed-form expression for the model-plant mismatch that may be present in a feedback control system. The main limitation on the expression is that the controller and plant models should be representable by means of transfer functions, i.e. they should be linear and time invariant. This includes a variety of controllers, among which the ubiquitous Proportional, Integral and Derivative (PID) controller. The expression can then be used to identify the true plant transfer function. The MPM expression is shown to work for single-input single-output as well as a multiple-input multiple-output systems.

Combined neural network and particle filter state estimation with application to a run-of-mine ore mill

Abstract A run-of-mine (ROM) ore milling circuit poses many difficulties in terms of measuring process variables and determining accurate models. Control of the ROM circuit is therefore not a trivial task to achieve. An example of a ROM circuit model with reduced complexity that works well for control purposes is discussed. The mill model is discussed in detail, as this model is used for state estimation. A neural network is trained with three disturbance parameters and used to estimate the internal states of the mill, and the results are compared with those of particle filter implementation. A novel combined neural network and particle filter state estimator is presented. The estimation performance of the neural network is promising when the disturbance magnitude used is smaller than that used to train the network.