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

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 per 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 throughputhttp://www.elsevier.com/locate/conengprachb201

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

Predicting optimal operating points by modelling different flotation mechanisms

The inverse relationship between grade and recovery in flotation circuits is well accepted, and forms the basis of control strategies used to maxisise recovery subject to grade specifications. The concept of peak air recovery however suggests that this relationship is more complex, and that a model of this peak could indicate an optimal operating point. In this paper froth stability is modelled using a combination of fundamental and empirical models. Potential applications of a non-linear model in optimising flotation performance investigated, and the benefits demonstrated.https://www.journals.elsevier.com/ifac-papersonlinepm2020Electrical, Electronic and Computer Engineerin

Economic hybrid non-linear model predictive control of a dual circuit induced draft cooling water system

Petrochemical plants require the addition and removal of energy to and from the process and the movement of material to, from, and within the process piping and vessels. These fundamental mass and energy transfer requirements are typically achieved through the use of process utilities, which include electricity, steam, fuel gas, cooling water and compressed air. Utilities are responsible for a significant portion of the operating cost of a plant. Therefore, reduction in the consumption of utilities is a common process optimisation area. The situation is different when it comes to the generation and transportation of these utilities, which are often overlooked with regard to optimisation. In this paper, the potential benefits of utility optimisation are illustrated with particular focus on the generation and transportation areas. The main objectives are reductions in electrical energy consumption and cost and are illustrated for a dual circuit cooling water system. This system is non-linear and also hybrid in the sense that it contains both continuous and discrete input variables, which significantly complicates the design and implementation of control and optimisation solutions. This paper illustrates how the cost and energy consumption of a hybrid system can be reduced through the implementation of hybrid non-linear model predictive control (HNMPC) and economic HNMPC (EHNMPC). The results are compared to that of a base case and an Advanced Regulatory Control (ARC) case, showing that significant additional benefit may be achieved through the implementation of these advanced control and optimisation techniques. The paper further illustrates that additional capital is not necessarily required for the implementation of these techniques.The National Research Foundation of South Africa (Grant Number 90533).http://www.elsevier.com/locate/jprocont2018-05-30Electrical, Electronic and Computer Engineerin

Coal dense medium separation dynamic and steady-state modelling for process control

Coal dense medium separation is a popular beneficiation process used for the upgrading of coal ore into power station and metallurgical coal. The control systems used in coal beneficiation are often limited to localised regulatory control of feed rate and medium density. A coal dense medium separation process can benefit substantially from process control provided that a dynamic model for this process is available as was previously developed by the authors for a fine coal dense medium cyclone (DMC) circuit. In this paper, the previous model is adapted to a coarse coal DMC circuit and validated over a wider range of operating conditions using real plant data. The model is further validated by reducing it to steady-state to form a partition curve. This curve is then compared to one derived from actual production data. The derived model is able to provide an estimate of the DMC overflow coal product that should be sufficient for process control.http://www.elsevier.com/locate/minenghb201

Dynamic model for a dense medium drum separator in coal beneficiation

Dense medium drum (DMD) separators are unit processes that are typically used to beneficiate coal, iron ore and other minerals by making use of density separation. Some coal dense medium separation plants typically include a DMD separator. The operational management of this unit process is often limited to localised control of medium density and feed mass flow rate. Dynamic models for coal dense medium separation have been developed by the authors with the intention of using them for dynamic control. A suitable dynamic model for a DMD separator could not been found in the available literature. This paper shows how the dynamic model for a dense medium cyclone has been applied to a DMD separator. The model parameters were determined and the performance of the model is evaluated using actual plant data from a Wemco drum. Coal washability and drum partitioning behaviour are used to estimate the grade of the product for model grade simulation and validation.http://www.elsevier.com/locate/minenghb201

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

Selective pinning control of the average disease transmissibility in an HIV contact network

Medication is applied to the HIV-infected nodes of high-risk contact networks with the aim of controlling the spread of disease to a predetermined maximum level. This intervention, known as pinning control, is performed both selectively and randomly in the network. These strategies are applied to 300 independent realizations per reference level of incidence on connected undirectional networks without isolated components and varying in size from 100 to 10 000 nodes per network. It is shown that a selective on-off pinning control strategy can control the networks studied with limited steady-state error and, comparing the medians of the doses from both strategies, uses 51.3% less medication than random pinning of all infected nodes. Selective pinning could possibly be used by public health specialists to identify the maximum level of HIV incidence in a population that can be achieved in a constrained funding environment.South African Centre for Epidemiological Modelling and Analysis (SACEMA).In part by the National Research Foundation of South Africa (Grant Number 90533).http://journals.aps.orghb201

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