Development of a Softsensor for Particle Size Monitoring

"Some key control variables of industrial processes, associated with product quality, often cannot be measured directly or frequently enough to establish adequate control. In such cases, it is possible to use available measurements to provide a prediction for these process variables and use them in a control strategy, thereby giving rise to what is now commonly called a softsensor.In some industrial grinding circuits, the on-line particle size analyzer is shared between various sampling points. Therefore, for a given location, the actual measurement is only available every 10 to 20 minutes, a delay which is unacceptable for automatic control purposes. To alleviate this problem, a softsensor based on an artificial neural network has been investigated. First, the structure of the neural network and different schemes for the training process are analyzed. Then, the performance of the neural network softsensor is compared with other inferential methods such as ARMA models and Kalman filters.IntroductionGRAHM, French acronym of the Research Group on Computer Applications in the Mineral Industry at Laval University, began in 1990 a research project on Knowledge Based Automatic Control (KBAC). This project, sponsored by a consortium of eight mining companies and the federal and provincial governments, seeked to screen advanced (knowledge-based) methods for data processing (filtering, mass balancing, etc) and automatic control (model-based) for their use in the mineral processing operations and the transfer of the project results to the sponsor companies.In parallel to more academic research work, a number of case-studies have been undertaken with the cooperation of partner companies. Among such works, a very successful one has been the design and installation of a new control strategy for one of the Kidd Creek (Falconbridge Ltd.) grinding circuits. This project was related to the evaluation of a number of control strategies and the implementation of one of them, as selected by the plant management, in the concentrator.A total of sixteen control schemes were evaluated on one of the dynamic phenomenological simulators developed at GRAHM, a trademark of this research group [ l]. The calibration of the simulator models was done based on industrial data gathered by GRAHM and Kidd Creek personnel. Then, the simulator was used to test the performance of various PID-based control schemes, designed to achieve the control objectives set by the Kidd Creek personnel. The best scheme proposed called for controlling the cyclone overflow particle size by manipulating the water addition rate to the cyclone feed box and the circulating load by manipulating the circuit ore feed. The particle size analyzer (PSI-200) available for monitoring the product size, was unfortunately shared (multiplexed) among various streams of the plant, which meant a 14 min delay between two consecutive readings for any particular stream. This induced the plant personnel to choose an alternative control strategy, one which controlled the cyclone feed density instead of the cyclone overflow particle size."