Development amd implementation of a real-time observer model for mineral processing circuits.

Abstract

Mineral processing plan ts, such as LONMIN's Eastern Platinum B-stream, typically have few on-line measurements, and key measures of performance such as grade only become available after samples have been analysed in the laboratory. More immediate feedback from a dynamic observer model promises enhanced understanding of the process, and facilitates prompt corrective actions, whether in open or closed loop . Such plant s easily enter sub-optimal modes such as large , uselessly re-circulating loads as the feed conditions change. Interpretation of such modes from key combinations of the variables deduced by an observer model , using a type of expert system, would add another level of intelligence to benefit operation. The aim of this thesis was to develop and implement a dynamic observer model of the LONMIN Eastern Platinum B-Stream into one of the existing control platforms available at the plant , known as PlantStar®, developed by MINTEK. The solution of the system of differential and algebraic equations resulting from this type of flowsheet modelling is based on an extended Kalman filter, which is able to dynamically reconcile any measurements which are presented to it, in real time. These measurement selections may also vary in real time, which provides flexibility of the model solution and the model 's uses. PlantStar passes the measurements that are available at the plant, to the dynamic observer model through a "plugin" module, which has been developed to incorporate the observer model and utilise the PlantStar control platform. In an on-line situation, the model will track the plant's behaviour and continuously update its position in real-time to ensure it follows the plant closely. This model would then be able to run simulations of the plant in parallel and could be used as a training facility for new operators, while in a real-time situation it could provide estimates of unmeasurable variables throughout the plant. An example of some of these variables are the flotation rate constants of minerals throughout the plant, which can be estimated in real time by the extended Kalman filter. The model could also be used to predict future plant conditions based on the current plant state , allowing for case scenarios to be performed without affecting the actual plant's performance. Once the dynamic observer model and "plugin" module were completed, case scenario simulations were performed using a measured data set from the plant as a starting point because real-time data were unavailable as the model was developed off-site .