Application of Statistical and Intelligent Techniques for Modeling of Metallurgical Performance of a Batch Flotation Process

Froth flotation is one of the most frequently used processes for separation of valuable from gangue minerals. Modeling and simulation of the flotation process is a difficult task because of nonlinear and dynamic nature of the process. In this contribution, the relationship between the process variables (i.e., gas flow rate, slurry solids%, frother/collector dosages, and pH) and the metallurgical parameters (i.e., copper/mass/water recoveries and concentrate grade) in the batch flotation of a copper sulfide ore is discussed and modeled. Statistical (i.e., nonlinear regression) and intelligent (i.e., neural network and adaptive neuro-fuzzy) techniques are applied to model the process behavior at different conditions. The results indicate that intelligent approaches are more efficient tools for modeling of the complicated process like flotation, which are of central importance for development of the model-based control systems.