(747e) Mixed Integer Linear Programming Optimization Framework Applied to a Platinum Group Metals Flotation Circuit

This research focuses on determining an optimum flotation circuit in terms of operating variables such as mass flowrates of flotation streams and structural variable like flotation circuit configuration. Determination of optimal circuit involves the use of Mixed-Integer Linear Programming framework in which a constrained objective function is formulated. The objective function in this work is maximization of Platinum Group Metals (PGMs) recovery from flotation circuit constrained by the quality specification of the final concentrate, i.e. gram of PGMs per ton of concentrate.

A superstructure was employed to provide a set of alternative circuits out of which an optimal one is obtained. Optimization was carried out on the basis of a developed flotation model. The model formulation begins with mass balance for each flotation cell and then integrates the cells to build the overall optimization model of the flotation circuit. Optimum operating conditions and structural variables such as concentrate flowrate and flotation circuit configuration respectively, were determined. Flotation process parameters such solid density and rate constants were also fitted in this model.

The results generated by the optimization algorithm show that high recoveries occur at rougher stages while low recoveries at cleaner stages. This high recovery at rougher stage is mainly attributed to the fact that recovery at this stage takes place through two mechanisms, namely: True flotation and Entrainment. Whilst at cleaner stages entrainment is unlikely to occur due to high froth heights in the cells. The increase in concentrate mass flowrate increases recovery with the assumption that the process is at steady state in terms of feed grade whilst other operating variables such as reagent dosage, particle sizes, and aeration rate are at optimal values.