Multiobjective Optimisation of Flotation Variables Using Controlled-NSGA-II and Paretosearch

Abstract

Finding the optimum operating points for the maximisation of flotation recovery and concentrate grade can be a very difficult task, owing to the inverse relationship that exists between these two key performance indicators. For this reason, techniques that can accurately find the trade-off are critical for flotation process optimisation. This work extracted well-assessed Gaussian process predictive functions as objective functions for a comparative multiobjective optimisation study using the paretosearch algorithm (PA) and the controlled elitist non-dominated sorting genetic algorithm (controlled-NSGA-II). The main aim was the concomitant maximisation of the copper recovery and the concentrate grade. Comparison of the two applied techniques revealed that the PA discovered the best set of the pareto-optimal solution for both the recovery (93.4%) and concentrate-grade (17.4 wt.%) maximisation.