Genetic algorithm optimization of supercritical fluid extraction of nimbin from neem seeds

The subject of this study is to optimize supercritical extraction of nimbin from neem seeds using a Genetic Algorithm (GA) technique. In order to investigate the effect of parameters on nimbin extraction yield, a partial differential equation model based on mass conservation was developed and solved...

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Vydáno v:Journal of food engineering Ročník 97; číslo 2; s. 127 - 134
Hlavní autoři: Zahedi, G., Elkamel, A., Lohi, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.03.2010
[New York, NY]: Elsevier Science Pub. Co
Elsevier
Témata:
algorithms
Azadirachta indica
Biological and medical sciences
carbon dioxide
chemical constituents of plants
diffusion
diffusivity
dynamic programming
flow resistance
Food engineering
Food industries
Fundamental and applied biological sciences. Psychology
General aspects
Genetic algorithm
mass transfer
medicinal plants
Modeling
Neem extraction
neem oil
neem seed extract
Optimization
quantitative analysis
seeds
Supercritical extraction
supercritical fluid extraction
Supercritical extraction
Neem extraction
Genetic algorithm
Modeling
Optimization
Seeds
Supercritical fluid extraction
ISSN:0260-8774, 1873-5770
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Shrnutí:The subject of this study is to optimize supercritical extraction of nimbin from neem seeds using a Genetic Algorithm (GA) technique. In order to investigate the effect of parameters on nimbin extraction yield, a partial differential equation model based on mass conservation was developed and solved numerically. The results were successfully validated and a parameter estimation problem that employs laboratory experimental data was solved. Using this validated model and the optimized set of parameters in the model, another problem was formulated with the aim of optimizing the extraction process. Profit was set as the objective function. Using a GA optimization algorithm, it was found that profit achieves its maximum when T = 305 k, P = 200 bar, carbon dioxide flow rate = 0.967 cm 3/min and d p = 0.1431 cm. The ability of the GA algorithm in optimizing the process was compared with a traditional Gradient Search (GS) optimization technique. THE GA technique proved to be a more efficient technique; especially when considering computational effort in reaching an optimal solution.
Bibliografie:http://dx.doi.org/10.1016/j.jfoodeng.2009.10.001
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ISSN:0260-8774
1873-5770
DOI:10.1016/j.jfoodeng.2009.10.001