ReviewMethods for automatic control, observation, and optimization in mineral processing plants
Introduction
This document provides an overview on the control methods that are available or practically used in mineral processing (MP) plants. It is partly an updated version based on a survey conducted a decade ago [1], but it does not aim at delivering an exhaustive literature survey. It rather gives a personal point of view developed along years from research activities done in collaboration with a multidisciplinary research team at Université Laval. Although the presentation scope is quite large, a limited number of references have been included. However, a book edited by Sbarbaro and del Villar [2] will soon be available and will describe in more depth many aspects presented here, thus offering a wider literature survey. Also, a recent survey done by Thwaites [3] gives the industrial point of view on MP automation needs.
At first, a brief description of the chain of processes involved in MP plants will be presented. It will recall the various process variables that are involved when controlling a MP process. Then, most basic control objectives will be formulated, and a general control scheme proposed. It incorporates model-based data processing, control, and optimization blocks that will be described in the next paper sections. Methods such as soft sensors, data reconciliation, fault detection, and feature extraction from images will be presented in the data processing section. An overview of the most common linear control methods will follow, putting the emphasis on model-based predictive controllers. Finally the main features of the optimization block that calculates optimal set-points to be fed to the controllers will be presented. Each method will be illustrated by one or more examples taken from the literature.
Section snippets
Mineral processing chain of processes
A raw ore cannot be used as such as a final product for industrial or commercial uses. It needs to be treated for preparing usable materials that can be either specific minerals released from the ore, or more usually metals, alloys, or compounds such as oxides. Although the steps of the transformation chain (see Fig. 1), which leads to the final metal, is a technically coherent sequence of processes, the present study mainly focuses on physical treatments of ores (grinding and flotation
Measurement processing block
An important aspect of MP control is the measurement processing, and the famous axiom Garbage In Garbage Out summarizes well this critical stage of the MP control loop. The data processing block is essential to the success of the next operations of the generalized control loop [5], since the processes are inherently stochastic and difficult to model, and the measurements quite inaccurate, while the most important variables are not measurable on-line. Fig. 6 shows more details on the data
Controllers
The control architectures presented below are neither new nor specific to MP plants, but they reflect the approach used for training undergraduate and graduate students in mineral processing and metallurgical engineering at Université Laval. The emphasis is put on discrete time design based on internal model structure and predictive control concept. This concept was simultaneously developed in France by Richalet [40] and in USA by Cutler and Ramaker [41]. Moreover, surveys based on this
Optimizers
As said above, with respect to the economic performance of a MP plant, the controller performance is most probably not as important as the right selection of the set-points. This is why it would be useful to supervise a regulatory control system with an optimizer that may change the operating set-points as a function of the properties and throughput of the processed ore, in addition to external factors such as metal market prices or environmental constraints. An MP plant performance can be
Conclusion
This paper aimed at describing the basic elements comprised in generalized control loops of mineral processing plants. The three main messages that were delivered are: (1) the controllers and optimizers are efficient when the observers have been carefully designed, taking into account that measurements are inevitably corrupted by various errors and that major variables such as liberation degree and particle hydrophobicity are not measurable, therefore requiring process models to compensate for
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