Five objective optimization using naïve & sorting genetic algorithm (NSGA) for green microalgae culture conditions for biodiesel production

被引：0
作者：
Eswari J.S. [1 ]
Tripathi M.K. [2 ]
Dhagat S. [1 ]
Karn S.K. [3 ]
机构：
[1] Department of Biotechnology, National Institute of Technology, Raipur
[2] Department of Metallurgical and Materials Engineering, National Institute of Technology, Raipur
[3] Department of Biochemistry & Biotechnology, Sardar Bhagwan Singh University, Dehradun
来源：
Recent Innovations in Chemical Engineering | 2019年 / 12卷 / 02期
关键词：
Biodiesel; Chlorophyll; Microalgae; Multi-objective optimization; Naïve and Sorting Genetic Algorithm Optimization (NSGA); Optimization;
D O I：
10.2174/2405520412666190124163629
中图分类号：
学科分类号：
摘要：
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 optimisa-tion 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%, Pro-tein=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. © 2019 Bentham Science Publishers.
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页码：110 / 121
页数：11
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