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Five objective optimization using Navie & Sorting Genetic Algorithim (NSGA) for green microalgae culture conditions for biodiesel production

Author(s):

J. Satya Eswari*, Manwendra Kumar Tripathi, Swasti Dhagat and Santosh Kr. Karn  

Abstract:


Renewable sources of energy like biodiesel are substitute energy fuel which made from renewable bio sources or biomasses. Because of many advantages related by using the algae (Chlorella spp) we performed design of experiments in terms of functional and biochemical factors are biomass, chlorophyll content and protein moiety and carbohydrate and lipid contents. Our objective is to maximization of lipid accumulation (y1) and chlorophyll content (y2), minimization of carbohydrate consumption (y3), protein (y4) and biomass (y5), content. By using the experimental data, 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 production of biodiesel as biomass which is renewable energy to improve the quality. The corresponding input and output conditions with multi-objective optimisation navie & sorting genetic algorithm (NSGA) algorithm is X1 = 0.99, X2=0.001, X3 = -1.111, X4 = 0.01 and Lipid= 42.34, Chlorophyll=1.1212, Carbohydrate= 24.54, Protein=0.0742, Biomass=0.999. The multi objective optimization NSGA prediction is compared with response surface model combined with genetic algorithm (RSM-GA) and we observed better productivity with NSGA.

Keywords:

Biodiesel, Optimization, Naïve and slow sorting Genetic algorithm optimization (NSGA), Multi objective optimization, chlorophyll

Affiliation:

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



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