Optimization of Venturi Scrubbers Using Genetic Algorithm

Optimization of a venturi scrubber was carried out using a nondominated sorting genetic algorithm (NSGA). Two objective functions, namely, (a) maximization of the overall collection efficiency η o and (b) minimization of the pressure drop Δ p, were used in this study. Three decision variables, the liquid−gas flow ratio L / G, the gas velocity in the throat V gth, and the aspect ratio Z were used. Optimal design curves (nondominated Pareto sets) were obtained for a pilot-scale scrubber. Values of the decision variables corresponding to optimum conditions on the Pareto set were obtained. It was found that the L / G ratio is a key decision variable that determines the uniformity of liquid distribution and the best values of L / G and Z are about 1.0 × 10 -3 and 2.5, respectively. In addition, V gth was found to vary from about 40 to 100 m/s as the optimal η o on the Pareto increased (as did Δ p ) from about 0.6 to 0.98. The effect of adding a fourth decision variable, the throat length L o, was also studied. It was found that this leads to slightly lower pressure drops for the same collection efficiency than obtained with three decision variables. An optimum length correlation for the throat of the venturi scrubber was obtained as a function of operating conditions. This study illustrates the applicability of NSGAs in solving multiobjective optimization problems involving gas−solid separations.