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Registros recuperados: 11 | |
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Garlapati,Vijay Kumar; Vundavilli,Pandu Ranga; Banerjee,Rintu. |
The aim of this work was to apply a modeling integrated optimisation approach for a complex, highly nonlinear system for an extracellular lipase extraction process. The model was developed using mutation, crossover and selection variables of Differential Evolution (DE) based on central composite design of Response Surface Methodology. The experimentally validated model was optimized by DE, a robust evolutionary optimization tool. A maximum lipase activity of 134.13 U/gds (more than 36.28 U/gds compared to one variable at a time approach) was observed with the DE-stated optimum values of 25.01% dimethyl sulfoxide concentration, 40 mM buffer, 128.52 min soaking time and 35ºC with the DE control parameters, namely number of population, generations, crossover... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Modeling; Optimization; Lipase extraction; Response surface methodology; Differential evolution; Solid state fermentation. |
Ano: 2013 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132013000500001 |
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Kumari,Annapurna; Mahapatra,Paramita; Banerjee,Rintu. |
Optimization of lipase production by Enterobacter aerogenes was carried out using response surface methodology (RSM) where the statistical model was obtained by fractional factorial central composite design. The influence of various physico-chemical parameters, viz. temperature, oil concentration, inoculum volume, pH and incubation period on lipase production was examined. Optimization of physico-chemical parameters resulted 1.4- fold increase in lipase activity. The optimum levels of parameters were 34°C, oil concentration 3%, inoculum volume 7%, pH 7 and incubation time 60 h for obtaining a maximum lipase activity of 27.25 U/ml. |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Central composite design; Enterobacter aerogenes; Lipase; Optimization; Parameter; Response Surface Methodology. |
Ano: 2009 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132009000600005 |
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Meena,Ganga Sahay; Kumar,Nitin; Majumdar,Gautam Chandra; Banerjee,Rintu; Meena,Pankaj Kumar; Yadav,Vijesh. |
The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Response surface methodology (RSM); Artificial neural network (ANN); Genetic algorithms (GA); Box-behnken besign (BBD). |
Ano: 2014 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132014000100003 |
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Registros recuperados: 11 | |
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