Abstract
In the flotation process, the concentrate grade and the tailing grade are crucial technical indices which reflect the product quality and efficiency. There are strong nonlinearity and uncertainty in such technical indices dynamic behaviors, which can hardly be described using accurate mathematical model. The technical indices which cannot be measured online continuously vary with boundary conditions. Therefore conventional control methods are incapable of keeping the actual the concentrate grade and the tailing grade within the target ranges. In this paper, an intelligent control method comprised of the setting layer and the closed loop control layer for the flotation reagent addition to the process has been presented. In flotation reagent feeding setting layer, a unit reagent pre-setting model, a feedback compensator and a feed forward compensator RBR based on (Rule-based reasoning) are integrated with a flotation reagent computation model to set the flotation reagent feeding. The control system updates automatically flotation reagent feeding when the boundary conditions changes. Successfully industrial application has shown that the concentrate grate has been increased by 0.52%, the tailing grade has been reduced by 4%, and the consumption of the flotation reagent feeding has been reduced by 17.5%. Significant application effect has been achieved. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.
Original language | English |
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Title of host publication | IFAC Proceedings Volumes (IFAC-PapersOnline)|IFAC Proc. Vol. (IFAC-PapersOnline) |
Volume | 17 |
DOIs | |
Publication status | Published - 2008 |
Event | 17th World Congress, International Federation of Automatic Control, IFAC - Seoul Duration: 1 Jul 2008 → … http://www.ifac-papersonline.net/Detailed/37247.html |
Conference
Conference | 17th World Congress, International Federation of Automatic Control, IFAC |
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City | Seoul |
Period | 1/07/08 → … |
Internet address |
Keywords
- Algorithms and software
- Industrial applications of optimal control