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.