Simultaneous optimization of the performance of flotation circuits and their simplification using the jumping gene adaptations of genetic algorithm

The elitist non-dominated sorting genetic algorithm with the modified jumping gene operator (NSGA-II-mJG) is used to optimize the performance of froth flotation circuits. Four example optimization problems (Mehrotra and Kapur, 1974; Green, 1984; Dey et al., 1989) Mehrotra, S.P., Kapur, P.C., 1974. Optimal–sub-optimal synthesis and design of flotation circuits. Sep. Sci. 9, 167–184; Green, J.C.A., 1984. The optimization of flotation networks. Int. J. Miner. Process. 13, 83–103; Dey, A.K., Kapur, P.C., Mehrotra, S.P., 1989. A search strategy for optimization of flotation circuits. Int. J. Miner. Process. 26, 73-93.] of varying complexity are solved using single-objective functions. In one example, the overall recovery of the concentrate stream is maximized for a desired grade of the concentrate and a fixed total cell volume. The interconnecting cell linkage parameters (fraction flow rates) and the mean cell residence times are the decision variables. In all these cases, the optimal solutions obtained using NSGA-II-mJG are superior to those obtained by earlier techniques (which converged to local optimal solutions). Thereafter, a few two-objective optimization problems are solved. In these, the performance of the circuit is optimized, and simultaneously, the number of connecting streams is minimized so as to give simple circuits. Pareto optimal sets of equally good (non-dominating) solutions are obtained. This is probably the first study involving the multi-objective optimization of flotation circuits with one aim being to simplify them.