Multi-objective optimization of microfiltration of baker's yeast using genetic algorithm

Abstract

This paper presents a multi-objective optimization model by applying genetic algorithm in order to search for optimal operating parameters of microfiltration of baker's yeast in the presence of static mixer as a turbulence promoter. The operating variables were the suspension concentration, transmembrane pressure, and feed flow rate. Two conflicting objective functions, maximizing the permeate flux and maximizing the reduction of energy consumption, were considered. This multi-objective optimization problem was solved by using the elitist non-dominated sorting genetic algorithm in the Matlab R2015b software. The Pareto fronts along with the process decision variables correspondding to the optimal solutions were obtained. It was found that lower suspension concentrations (2-4.5 g/L), feed flow rate in the range 109-127 L/h, and transmembrane pressure of 1 bar were the optimal process parameters which yielded maximum permeate flux (177-191 L/(m2h)) and maximum reduction of energy consumption (44-50%). Finally, the results were compared with the previously published results obtained by applying desirability function approach. Given that genetic algorithms have generated multiple solutions in a single optimization run, the study proved that genetic algorithms are preferable to classical optimization methods.