Optimization of liquid-liquid Extraction Column Using Genetic Algorithms


In the present study, liquid-liquid extraction column was optimized using GeneticAlgorithms as a non-conventional optimization technique, which scores overconventional techniques. Genetic Algorithm (GA) is a stochastic search techniquemimics the principle of natural genetics and natural selection to constitute search andoptimization. Genetic Algorithm is applied to the optimal design of liquid-liquidextraction column to maximize the extraction rate using the superficial velocities ofraffinate and extract phases, (υx, υy) respectively as design variables using Matlab GAtoolbox. Different Genetic Algorithm strategies were used for optimization and thedesign parameters such as Population size, crossover rate and Mutation were studied. Itwas found that for constant distribution coefficient, m the convergence is obtained in avery few generations (51 generations). The effect of distribution coefficient, m wasalso studied on the optimization process and found that when increasing thedistribution coefficient the optimum extraction rate increased. The best values for υxand υy were 0.142 and 0.059 respectively, and the objective function (maximum) was0.2844187.