Estimation of flotation rate constant and collision efficiency using regression and artificial neural networks

The effects of particle characteristics and hydrodynamic conditions on the flotation rate constant (k) and bubble-particle collision efficiency (E-c) of pyrite and chalcopyrite particles were investigated. Experimental results showed that k increases with increase of bubble surface area flux (S-b) and E-c. Artificial neural network (ANN) and multivariable linear regression procedures were used to predict both k and E-c based on the particle characteristics and hydrodynamic conditions. Multivariable linear regression resulted in R-2 of 0.6 and 0.93 for k and E-c, respectively. Using an ANN model, R-2 as high as 0.98 was achieved in modeling the E-c with regard to the available parameters. The proposed ANN model can be reliably used to determine both k and E-c parameters in froth flotation.