Kinetic modeling and optimization of flotation process in a cyclonic microbubble flotation column using composite central design methodology

In this work, a composite central design with five levels and four variables was employed to model and optimize the batch flotation kinetic process in a cyclonic microbubble flotation column (FCMC). 30 sets of batch flotation rate tests were executed at different conditions of pulp concentration (X1), frother dosage (X2), flow rate of circulation pulp (X3) and froth depth (X4). It was observed the maximum flotation time (tmax) obtained in tests fluctuated wildly under different conditions. Statistical analysis based on the model fit and stability was performed to discriminate six kinetic models. The response surface methodology was used for the identification and development of significant relationship between process variables. Statistical analysis indicated that the modified Kelsall model was the optimal kinetic model for characterizing the flotation process. Analysis of variance results revealed that the effect of X1 was significant for all process responses. X4 was found as a significant independent factor for the two response variables of tmax and the ultimate combustible recovery (ε∞) of the optimal kinetic model. X3 had a significant influence on the parameter of the optimal kinetic model (the fraction of flotation components with the slow rate constant). Furthermore, the maximum flotation time and ε∞ were significantly influenced by the interaction between X1 and X4. Based on the result of optimization it was found that the desired ultimate combustible recovery with an appropriate flotation time was obtained from the flotation process with a given range of experimental variables (X1: from the intermediate levels to the higher levels; X2: the intermediate level; X3: 220 g/t and X4: 25.00 mm). There was an acceptable relationship between predicted and actual values with one of the optimal conditions (Adj. R2 = 0.9971). The response surface methodology was effective for predicting and optimizing the batch flotation process of FCMC.