Rapid estimation of floatability components in industrial flotation plants

Abstract

In the analysis of industrial flotation plant data, it is advantageous to build a reliable computer based flotation model to be used in optimisation and design. A number of models have been proposed by many authors with each model having value depending on the complexity of the situation for which it was developed. Several of these models assume that a floatability distribution of a mineral entering a flotation circuit can be described by a number of floatability classes or components with each of these components (and its flotation rate constant) being conserved around the circuit. A non-linear optimisation procedure for regressing floatability parameters (number of floatability components, and the flotation rate constant of each) from laboratory batch flotation tests conducted on streams around the circuit has recently been devised [1]. This paper outlines a technique to linearise the above regression procedure which improves the ease of solution and the level of confidence in the floatability parameter determination. The technique also offers a strategy for determining the “optimum” number of floatability components. Dummy and real plant data were used to test the validity of the new linear modelling technique. The dummy data were generated using a set flowsheet and a feed stream having two components (with set first order rate constants) to simulate the plant. The simulated data obtained were then fitted with the general linear model technique. The results of this operation show that the linear modelling procedure can obtain the original model parameters very well. The model was then tested on real plant data from three typical flotation plants. Again, there is a very good correlation between stream experimental and model calculated recoveries based on the feed to the circuit. The confidence limits of the model parameters generated using this technique are reported. The ability to estimate parameter confidence intervals indicates where additional data (or an independent method) are required for more accurate parameter determination.