Implementation of Gray Wolf Optimization algorithm to recycled gas centrifuge cascades

Abstract Since the separative power of a single centrifuge is relatively small, to achieve adequate enrichment and throughput, a large number of centrifuges are interconnected to form a cascade. An enrichment plant usually holds thousands of centrifuges. The pattern of connections is determined by the properties of the individual machines, the required quantity, and the concentration of the final product. Because the most separation costs of a cascade are proportional to the number of machines, it is required to determine the optimal operating parameters of the cascade to reduce the number of centrifuge machines. In a problem in which the number of variables is large, the amount of possible arrangement is truly astronomical, and a direct exhaustive search for the best pattern would be prohibitive. In recent years, nature-inspired meta-heuristic optimization algorithms have been developed to treat complicated engineering problems. In this paper, the Gray Wolf Optimization (GWO) algorithm based on the behavior of gray wolves for hunting is implemented to multi-objective optimization of gas centrifuge cascades. In this analysis, the performance of the algorithm is examined for challenging test functions, primarily; and then several real cascades are evaluated and the results are compared with other methods. The GWO is used to minimize the number of machines and optimum operational parameters of cascades for binary mixture separations. It is found from an evolutionary point of view, the performance of the GWO as an efficient method is quite adequate.

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