Author
Abstract
In the present study, liquid-liquid extraction column was optimized using Genetic
Algorithms as a non-conventional optimization technique, which scores over
conventional techniques. Genetic Algorithm (GA) is a stochastic search technique
mimics the principle of natural genetics and natural selection to constitute search and
optimization. Genetic Algorithm is applied to the optimal design of liquid-liquid
extraction column to maximize the extraction rate using the superficial velocities of
raffinate and extract phases, (υx, υy) respectively as design variables using Matlab GA
toolbox. Different Genetic Algorithm strategies were used for optimization and the
design parameters such as Population size, crossover rate and Mutation were studied. It
was found that for constant distribution coefficient, m the convergence is obtained in a
very few generations (51 generations). The effect of distribution coefficient, m was
also studied on the optimization process and found that when increasing the
distribution coefficient the optimum extraction rate increased. The best values for υx
and υy were 0.142 and 0.059 respectively, and the objective function (maximum) was
0.2844187.
Keywords
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