Five Objective Optimization Using Naïve & Sorting Genetic Algorithm (NSGA) for Green Microalgae Culture Conditions for Biodiesel Production

Background: Renewable sources of energy like biodiesel are substitute energy fuel which are made from renewable bio sources or biomasses. Due to many advantages of using algae (Chlorella sp), we performed design of experiments in terms of functional and biochemical factors such as biomass, chlorophyll content, protein moiety and carbohydrate and lipid contents. Objective: Our objective is maximization of lipid accumulation (y1) and chlorophyll content (y2) and minimization of carbohydrate consumption (y3), protein (y4) and biomass (y5) contents. By using the experimental data, the regression model has been developed in order to obtain the desired response (biomass, chlorophyll, protein, carbohydrate and lipid) therefore it is necessary to optimize input conditions. The pre-optimization stage is an important part and useful for the production of biodiesel as biomass which is renewable energy to improve the quality. Methodology: The corresponding input and output conditions with multi-objective optimisation using naïve & sorting genetic algorithm (NSGA) is X1=0.99, X2=0.001, X3=-1.111, X4=0.01 and Lipid= 42.34, Chlorophyll=1.1212 (μgmL-1), Carbohydrate= 24.54%, Protein= 0.0742 (mgmL-1), Biomass=0.999 (gL-1). Conclusion: The multi-objective optimization NSGA prediction is compared with the response surface model combined with a genetic algorithm (RSM-GA) and we observed better productivity with NSGA.