A search strategy for optimization of flotation circuits

Abstract

We present a search technique which is tailored for the design of optimum flotation circuits from a generalized master network. The strategy is based on direct search driven by skewed random numbers. Depending on the drift of the parameters, a newly devised algorithm continuously adjusts the bias in the search, resulting in significantly accelerated convergence to the optimum or apparent optimum circuit configuration. This search strategy has been successfully tested under a wide variety of arbitrary sets of process, structural and economic constraints embedded in non-linear mixed-integer optimization problems of varying size and complexity. Some preliminary results are presented for verification. These include a reexamination of the optimum circuit design carried out by Green (1984) by a linear programming method. It is shown that there are no unique optimum circuits for this class of problems. In fact it is possible to synthesize much simpler circuits, having significantly smaller material hold-ups, which perform equally well, and also satisfy all the constraints imposed on the system. In the second example, a linear function of recovery and grade has been maximized. The ratio of weights on grade and recovery is varied from 10:1 to 1:10 in suitable steps. The optimum 3-bank circuit configuration changes from a rougher-clearner-cleaner to a rougher-scavenger-cleaner and finally to a rougher-scavenger-scavenger configuration, as one would intuitively anticipate. Moreover, the grade-recovery curve also varies smoothly in the expected manner. Interestingly, these circuits exhibit many features which are in conformity with a number of more or less empirical rules that have evolved over the years for the optimum design and operation of flotation circuits. Some of the more important implications of the design and synthesis methodology are briefly discussed.