Abstract
Flotation is an essential process in beneficiation production. The amount of flotation reagent has a significant influence on the quality of the product. An inappropriate dosing system will lead to metal loss and reagent waste, especially when the nature of the raw ore changes frequently. If the dosing system is not updated in time, it will cause economic losses. Based on digital twin technology and machine learning algorithms, this research designed a digital twin system for iron reverse flotation reagents. Based on the flotation froth image and transformer algorithm, a soft sensor model of tailings grade is established to monitor the product quality in real-time. The flotation dosing model established based on the ELM algorithm automatically updates the reagent system and intelligently assigns the controller. On the basis of stabilizing product quality, this research avoids the waste of reagents and improves the economic benefits of production efficiency. The system was applied in an iron flotation plant, and industrial operation effect verified the method.
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