02794nas a2200277 4500008004100000022001400041245014800055210006900203260009900272300001400371490000700385520173200392653003002124653002202154653001702176653002202193653002802215653002302243653002002266100002102286700002002307700002202327700001802349700001702367856013202384 2006 eng d a1359-511300aOptimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques0 aOptimization of fermentation media for exopolysaccharide product aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcAUG a1842-18480 v413 a

A Lactobacillus strain was isolated from the fermented Eleusine coracana. This strain was characterized as Lactobacillus plantarum and was found to produce an exopolysaccharide (EPS) in quantitative amounts. The objective of the present paper is to determine optimum media composition and inoculum volume for the stated fermentative production of the EPS. A hybrid methodology comprising the Plackett-Burman (PB) design method, artificial neural networks (ANN) and genetic algorithms (GA) was utilized. Specifically, the PB, ANN and GA forrnalisms were used for identifying influential media components, modeling non-linear process and optimizing the process, respectively. More specifically, the PB method was used to determine those media components, which significantly influence the EPS yield. By ignoring the less influential media components, the dimensionality of the input space of the process model could be reduced significantly. Out of the five media components only three were found influential namely, lactose, casein hydrolysate and triammonium citrate. Next, an ANN-based process model was developed for approximating the non-linear relationship between the fermentation operating variables and the EPS yield. The average % error and correlation coefficient for the developed ANN model were 4.8 and 0.999, respectively. The input parameters of ANN model were subsequently optimized using the GA formalism for obtaining maximum EPS yield in batch fermentation. The optimized media composition has predicted the yield of 7.01 g/l. The GA-optimized solution comprising media composition and inoculum volume was verified experimentally and it comes out be 7.14 g/l. (c) 2006 Elsevier Ltd. All rights reserved.

10aArtificial neural network10aexopolysaccharide10aFermentation10aGenetic algorithm10aLactobacillus plantarum10aMedia optimization10aPlackett-Burman1 aDesai, Kiran, M.1 aAkolkar, S., K.1 aBadhe, Yogesh, P.1 aTambe, S., S.1 aLele, S., S. uhttps://library.ncl.res.in/content/optimization-fermentation-media-exopolysaccharide-production-lactobacillus-plantarum-using-002362nas a2200277 4500008004100000022001400041245014500055210006900200260008000269300001200349490000700361520130400368653003001672653001801702653002201720653002301742653003301765653001601798100002101814700002801835700002101863700002201884700002101906700002601927856013101953 2009 eng d a1367-543500aMedia optimization for biosurfactant production by rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach0 aMedia optimization for biosurfactant production by rhodococcus e aTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANYbSPRINGER HEIDELBERGcMAY a747-7560 v363 a

This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques-(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)-were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI24)] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were similar to 3 and similar to 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were similar to 0.99 and similar to 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.

10aArtificial neural network10aBiosurfactant10aGenetic algorithm10aMedia optimization10aResponse surface methodology10aRhodococcus1 aPal, Moumita, P.1 aVaidya, Bhalchandra, K.1 aDesai, Kiran, M.1 aJoshi, Renuka, M.1 aNene, Sanjay, N.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/media-optimization-biosurfactant-production-rhodococcus-erythropolis-mtcc-2794-artificial-002701nas a2200241 4500008004100000022001400041245014500055210006900200260000900269300001200278490000800290520183600298653002102134653002102155653002202176653002202198653002002220653002502240100002602265700001602291700002102307856013102328 2015 eng d a0009-250900aEmbedded multiple shooting methodology in a genetic algorithm framework for parameter estimation and state identification of complex systems0 aEmbedded multiple shooting methodology in a genetic algorithm fr cSEP  a605-6180 v1343 a

A novel parameter estimation and state identification algorithm for nonlinear dynamical systems from data by embedding a multiple shooting methodology in the framework of a genetic algorithm (EMSGA) is described. The advantages of EMSGA are brought out by studies with two highly nonlinear examples, viz., the chaotic dynamics of a non-isothermal CSTR and the glycolysis regulation in Lactococcus lactis for production of lactic acid. For the chaotic dynamics with extremely high sensitivity to parameter values and initial conditions, EMSGA accurately estimates all process parameters while at the same time recovering the true dynamics of monitored and unmonitored variables from limited extents of noisy dynamic data. The superiority in accuracy and computational time of EMSGA over standalone genetic or multiple shooting algorithms for parameter estimation is shown for comparison purposes. In fact, EMSGA adapts well to the use of generalized canonical models, e.g., the S-system, where use of the fundamental Taylor series method of any order for integration becomes possible. This makes EMSGA highly efficient and exemplified here by estimating all the parameters of the derived higher dimensional 5-system model for the CSTR. Comparative studies with a well-known global-local enhanced scatter search method corroborate the suitability and advantages of the EMSGA methodology. For the glycolysis regulation example the numerical robustness of EMSGA is brought out by estimating a large number of model parameters when a majority of them are raised to the power of the state variables. Interestingly, we show that from noisy in vivo dynamic data it becomes possible to completely recover the noise-free dynamical behavior of unmonitored species concentrations using EMSGA. (C) 2015 Elsevier Ltd. All rights reserved.

10aCanonical models10aChaotic dynamics10aGenetic algorithm10aMultiple shooting10aNoise reduction10aparameter estimation1 aSarode, Ketan, Dinkar1 aKumar, Ravi1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/embedded-multiple-shooting-methodology-genetic-algorithm-framework-parameter-estimation-and