Optimization of liquid-liquid Extraction Column Using Genetic Algorithms - Ali D. Ali (Engineering and Technology Journal, 2012)
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.