STATISTICAL DISCRIMINATION OF FLOTATION MODELS BASED ON BATCH FLOTATION DATA

An essential requirement for the optimization of design and operation of a flotation facility is the availability of a mathematical model that predicts how the amount of recovery of desirable particles during a certain time period depends on the design parameters. The selection of a best model from a collection of proposed models is a choice among competing theories and is based on empirical results obtained from experimental data. This paper focuses on how a choice for the appropriate model may be based on the statistical analysis of recovery data. Two general statistical criteria for model discrimination are considered based on the model's predictive capability. They are referred to as model fit and model stability. Statistical measures relating to these criteria are calculated for five different time-recovery profiles to evaluate five different flotation models. These calculated measures are used to infer about the suitability of these proposed models in representing experimental data.