13 research outputs found
Gaussian process based model predictive control to address uncertain milling circuit dynamics
Model predictive control performance rests heavily on the accuracy of the available plant model. To address (possibly) time-variant model uncertainty, a nominal nonlinear state-space model is combined with an additive residual model that takes the form of a Gaussian process. With sufficient operational data the Gaussian process model is able to effectively describe the residual model error and reduce the overall prediction error for effective model predictive control. The efficacy of the method is illustrated using a milling circuit simulator.https://www.journals.elsevier.com/ifac-papersonlineam2022Electrical, Electronic and Computer Engineerin
Peripheral control tools for a run-of-mine ore milling circuit
Run-of-mine ore milling circuits are generally difficult to control owing to the presence of strong external disturbances, poor process models and the unavailability of important process variable measurements. These shortcomings are common for processes in the mineral-processing industry. For processes that fall into this class, the peripheral control tools in the control loop are considered to be as important as the controller itself. This work addresses the implementation of peripheral control tools on a run-of-mine ore milling circuit to help overcome the deteriorated control performance resulting from the aforementioned shortcomings. The effects of strong external disturbances are suppressed through the application of a disturbance observer. A fractional order disturbance observer is also implemented and a novel Bode ideal cutoff disturbance observer is introduced. The issue of poor process models is addressed through the detection of significant mismatch between the actual plant and the available model from process data. A closed-form expression is given for the case where the controller has a transfer function. If the controller does not have a transfer function, a partial correlation analysis is used to detect the transfer function elements in the model transfer function matrix that contain significant mismatch. The mill states and important mill parameters are estimated with the use of particle filters. Simultaneous state and parameter estimation is compared with a novel dual particle filtering scheme. A sensitivity analysis shows the class of systems for which dual estimation would provide superiorestimation accuracy over simultaneous estimation. The implemented peripheral control tools show promise for current milling circuits where proportional-integral-derivative (PID) control is prevalent, and also for advanced control strategies, such as model predictive control, which are expected to become more common in the future. AFRIKAANS : Maalkringe wat onbehandelde erts maal is oor die algemeen moeilik om te beheer as gevolg van die teenwoordigheid van sterk eksterne steurings, onakkurate aanlegmodelle en metings van belangrike prosesveranderlikes wat ontbreek. Hierdie probleme is algemeen vir aanlegte in die mineraalprosesseringsbedryf. Vir aanlegte in hierdie klas word die randbeheerinstrumente as net so belangrik as die beheerder beskou. Hierdie verhandeling beskryf die implementering van randbeheerinstrumente vir ’n maalkring wat onbehandelde erts maal, om die verswakte beheerverrigting teen te werk wat veroorsaak word deur bogenoemde probleme. Die impak van sterk eksterne steurings word teengewerk deur die implementering van ’n steuringsafskatter. ’n Breuk-orde-steuringsafskatter is ook geïmplementeer en ’n nuwe Bode ideale afsnysteuringsafskatter word voorgestel. Die kwessie van onakkurate aanlegmodelle word hanteer deur van die aanlegdata af vas te stel of daar ’n verskil is tussen die aanleg en die beskikbare model van die aanleg. ’n Uitdrukking word gegee vir hierdie verskil vir die geval waar die beheerder met ’n oordragsfunksie voorgestel kan word. Indien die beheerder nie ’n oordragsfunksie het nie, word van ‘n parsiële korrelasie-analise gebruik gemaak om die element, of elemente, in die aanleg se oordragsfunksiematriks te identifiseer wat van die werklike aanleg verskil. Die toestande en belangrike parameters in die meul word beraam deur van partikel-filters gebruikte maak. Gelyktydige toestand- en parameter-beraming word vergelyk met ’n nuwe dubbel-partikelfilter skema. ’n Sensitiwiteitsanalise wys die klas van stelsels waarvoor dubbel-afskatting meer akkurate waardes sal gee as gelyktydige afskatting. Die voorgestelde randbeheerinstrumente is toepaslik vir huidige maalkringe waar PID-beheer algemeen is, asook vir gevorderde beheerstrategieë, soos model-voorspellende beheer, wat na verwagting in die toekoms meer algemeen sal word. CopyrightDissertation (MEng)--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte
Development and application of a model-plant mismatch expression for linear time-invariant systems
When a plant and its controller are sufficiently linear and time-invariant so that they can be representedby transfer functions, and this plant is under classical control (meaning the controller can also be repre-sented by a transfer function), the model-plant mismatch (MPM) that often plagues industrial processescan be written as a closed-form expression. This includes a variety of controllers, among which the ubiq-uitous Proportional, Integral and Derivative (PID) controller. The MPM expression can then be used toidentify a representative transfer function of the “true plant” from the currently available plant model.The MPM expression works for single-input single-output as well as multiple-input multiple-output sys-tems. The closed-loop data required for application of the expression has to be sufficiently exciting. Ifsignificant disturbances perturb the plant their values need to be available. In this article the expressionis applied to industrial data to show its applicability.http://www.elsevier.com/locate/jprocont2016-08-31hb201
Fault-tolerant nonlinear MPC using particle filtering
A fault-tolerant nonlinear model predictive controller (FT-NMPC) is presented in this paper. State
estimates, required by the NMPC, are generated with the use of a particle filter. Faults are identiced with the nonlinear
generalized likelihood ratio method (NL-GLR), for which a bank of particle filters is used to generate the required fault
innovations and covariance matrices. A simulated grinding mill circuit serves as the platform for illustrating the use of
this fault detection and isolation (FDI) scheme along with the NMPC. The results indicate that faults can be correctly
identiced and compensated for in the NMPC framework to achieve optimal performance in the presence of faults.National Research Foundation of South Africa (Grant Number 90533).https://www.journals.elsevier.com/ifac-papersonline2017-07-31hb2017Electrical, Electronic and Computer Engineerin
Model-plant mismatch detection and model update for a run-of-mine ore milling circuit under model predictive control
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.
Once significant mismatch is detected the model may be partially re-identified
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,
and the process model subsequently updated.http://www.elsevier.com/locate/jprocontam2013ai201
Dual particle filters for state and parameter estimation with application to a run-of-mine ore mill
Measurements are not readily available for grinding mills owing to the nature of the milling operation.
State and parameter estimation for a grinding mill which forms part of a run-of-mine ore milling circuit
has been implemented. These estimates may then be used in an advanced control algorithm. The estimation
was done with dual particle filters as well as with a simultaneous estimation scheme, on simulated
data, to compare the performances. The sensitivity analyses for the different schemes show the class of
systems in which dual estimation may produce superior results.The University
of Pretoria postgraduate study abroad bursary programhttp://www.elsevier.com/locate/jprocontai201
Deep convolutional neural network for mill feed size characterization
Knowing the characteristics of the feed ore size is an important consideration for operations and control of a run-of-mine ore milling circuit. Large feed ore variations are important to detect as they require intervention, whether it be manual by the operator or by an automatic controller. A deep convolutional neural network is used in this work to classify the feed ore images into one of four classes. A VGG16 architecture is used and the classifier is trained making use of transfer learning.The National Research Foundation of South Africahttps://www.journals.elsevier.com/ifac-papersonlineam2020Electrical, Electronic and Computer Engineerin
Estimating ore particle size distribution using a deep convolutional neural network
In this work the ore particle size distribution is estimated from an input image of the
ore. The normalized weight of ore in each of 10 size classes is reported with good accuracy. A
deep convolutional neural network, making use of the VGG16 architecture, is deployed for this
task. The goal of using this method is to achieve accurate results without the need for rigorous
parameter selection, as is needed with traditional computer vision approaches to this problem.
The feed ore particle size distribution has an impact on the performance and control of minerals
processing operations. When the ore size distribution undergoes significant changes, operational
intervention is usually required (either by the operator or by an automatic controller).The National Research Foundation of South Africahttps://www.journals.elsevier.com/ifac-papersonlinepm2021Electrical, Electronic and Computer Engineerin
Towards an access economy model for industrial process control : a bulk tailings treatment plant case study
A nonlinear model for the surge tank of Sibanye-Stillwater’s Platinum tailings treatment plant is derived and linearised. Three controllers (two classical feedback and one model predictive controller (MPC)) are presented for control of the plant, and it is shown that a decoupled proportional-integral (PI) control structure, as would be employed in practice, performs the worst, while a nonlinear MPC controller provides the best performance. To illustrate an access economy model concept for industrial process control, a cloud platform to facilitate the competition between various controllers is presented and a scenario given with the three controllers competing to control the surge tank process. The platform is shown to provide the plant access to a controller that performs better than what is available locally.https://www.journals.elsevier.com/ifac-papersonlineam2022Electrical, Electronic and Computer Engineerin
A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes
The selection of an appropriate descriptive system and modeling framework to capture system dynamics and support process control applications is a fundamental problem in the operation of industrial processes. In this study, to account for the highly complex dynamics of industrial process and additional requirements imposed by smart and optimal manufacturing systems, an extended state space descriptive system, named comprehensive state space, is first designed. Then, based on the descriptive system, a hybrid first principles/machine learning modeling framework is proposed. The hybrid model is formulated as a combination of a nominal term and a deviation term. The nominal term covers the underlying physicochemical principles. The deviation term handles the effects of high-dimensional influence factors using regression of low-dimensional deep process features. To handle the multimodal and time-varying properties of process dynamics, the comprehensive state space is divided into subspaces indicating different operating conditions. The model parameters are identified and trained for each operating condition to form the sub-models. Then the system dynamics are formulated as a weighted sum of sub-models, with the weights being the probabilities that the current operating point belongs to different operating conditions. The weights update with the movement of the operating point in the comprehensive state space. Moreover, the descriptive system provides a platform for visualization, and can act as a digital twin of the physical process. A case study illustrates the feasibility and performance of the proposed descriptive system.The Projects of International Cooperation and Exchanges NSFC (grant no. 61860206014), the National Natural Science Foundation of China (grant nos. 61603418, 61973321, 61703441), the 111 Project (B17048), the Natural Science Foundation of Hunan Province (grant no. 2019JJ50823), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (grant no. 61621062), and the Major Program of the National Natural Science Foundation of China (grant no. 61590921).http://www.elsevier.com/locate/jprocont2021-02-01hj2020Electrical, Electronic and Computer Engineerin