In coal flotation, particles of different components of the coal such as maceral groups and mineral matter and their associations have different hydrophobicities and therefore different flotation responses. By using a new coal grain analysis method for characterising individual grains, more detailed flotation performance analysis and modelling approaches have been developed. The method involves the use of microscopic imaging techniques to obtain estimates of size, compositional and density information on individual grains of fine coal. The density and composition partitioning of coal processed through different flotation systems provides an avenue to pinpoint the actual cause of poor process performance so that corrective action may be initiated.
The information on grain size, density and composition is being used as input data to develop more detailed flotation process models to provide better predictions of process performance for both mechanical and column flotation devices. A number of approaches may be taken to flotation modelling such as the probability approach and the kinetic model approach or a combination of the two. In the work reported here, a simple probability approach has been taken, which will be further refined in due course. The use of grain data to map the responses of different types of coal grains through various fine coal cleaning processes provided a more advanced diagnostic capability for fine coal cleaning circuits. This enabled flotation performance curves analogous to partition curves for density separators to be produced for flotation devices.