J. Satya Eswari*, Manwendra Kumar Tripathi, Swasti Dhagat and Santosh Kr. Karn Pages 1 - 12 ( 12 )
Background: Renewable sources of energy like biodiesel are substitute energy fuel which is made from renewable bio sources or biomasses. Due to many advantages of using the algae (Chlorella sp), we performed the 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 the 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 naive & 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.
Biodiesel, Optimization, Naïve and slow sorting Genetic algorithm optimization (NSGA), Multi objective optimization, chlorophyll
Department of Biotechnology, National Institute of Technology, Raipur, Department of Metallurgical Engineering, National Institute of Technology, Raipur, Department of Biotechnology, National Institute of Technology, Raipur, Department of Biotechnology, National Institute of Technology, Raipur