
Optimal synthesis of an industrial fluorspar beneficiation plant using a jumping gene adaptation of genetic algorithm
Minerals & Metallurgical Processing, 2009, Vol. 26, No. 4, pp. 187202
Guria, C.; Varma, M.; Gupta, S.K.; Mehrotra, S.P.
ABSTRACT:
A modeling, simulation and optimization study of a complex industrial fluorspar beneficiation plant (Kadipani, Gujarat, India) with fourteen/ten flotation banks is carried out to find an optimum/nearoptimum circuit for the given quality of feed ore. The phenomenological flotation model developed by Mehrotra and Kapur (1974) is used for circuit optimization. Two different optimization problems are formulated and solved. The first optimization problem involves a single objective function, which uses available plant data to estimate the feedcharacterizing parameters, i.e., the flotation rate constants, by minimizing the weighted sumofsquare errors between the computed and plant values. The second optimization problem involves two objective functions to obtain several simplified circuits. The number of nonlinking streams and the overall recovery of the concentrate, i.e., the acidgrade product, are maximized simultaneously in this problem. The singleobjective variant (SGAIImJG) of the binarycoded elitist nondominated sorting genetic algorithm with the jumping gene adaptation, NSGAIImJG, is used for the first problem, while NAGAIImJG (Guria et al., 2005 a) is used for the second problem. Simplified circuits with a few split streams are proposed on the basis of the present optimization study, which enhance the recovery of the acidgrade product without affecting its quality, for a given total volume of the flotation cellbank. Even though the optimal solutions have certain drawbacks, they suggest a meaningful direction in using an iterative fitting of parameters to such optimal circuits, followed by reoptimization.

