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-002077nas a2200241 4500008004100000020002200041022001400063245008600077210006900163260008300232300001200315490000700327520121200334653001701546653001401563653002201577653003501599100002101634700002201655700001801677700002101695856011901716 2006 eng d a978-981-270-379-8 a0376-469900aSoft-sensor development for fed-batch bioreactors using support vector regression0 aSoftsensor development for fedbatch bioreactors using support ve aDR K S KRISHNAN MARG, NEW DELHI 110 012, INDIAbIndian Assoc Cultivat ScicJAN a225-2390 v273 a

In the present paper, a state-of-the-art machine learning based modeling formalism known as "support vector regression (SVR)", has been introduced for the soft-sensor applications in the fed-batch processes. The SVR method possesses a number of attractive properties such as a strong statistical basis, convergence to the unique global minimum and an improved generalization performance by the approximated function. Also, the structure and parameters of an SVR model can be interpreted in terms of the training data. The efficacy of the SVR formalism for the soft-sensor development task has been demonstrated by considering two simulated bio-processes namely, invertase and streptokinase. Additionally, the performance of the SVR based soft-sensors is rigorously compared with those developed using the multilayer perceptron and radial basis function neural networks. The results presented here clearly indicate that the SVR is an attractive alternative to artificial neural networks for the development of soft-sensors in bioprocesses.

10adeprotection10aMicrowave10astannous chloride10atert-butyldimethylsilyl ethers1 aDesai, Kiran, M.1 aBadhe, Yogesh, P.1 aTambe, S., S.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/soft-sensor-development-fed-batch-bioreactors-using-support-vector-regression-202577nas a2200277 4500008004100000022001400041245008200055210006900137260009900206300001200305490000700317520157600324653003001900653003201930653003601962100002401998700001802022700002202040700001902062700001602081700002202097700002002119700002302139700002602162856011102188 2007 eng d a0016-236100aEstimation of gross calorific value of coals using artificial neural networks0 aEstimation of gross calorific value of coals using artificial ne aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcFEB a334-3440 v863 a

The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels. (c) 2006 Elsevier Ltd. All rights reserved.

10aArtificial neural network10agross calorific value (GCV)10aproximate and ultimate analyses1 aPatel, Shagufta, U.1 aKumar, Jeevan1 aBadhe, Yogesh, P.1 aSharma, B., K.1 aSaha, Sujan1 aBiswas, Subhasish1 aChaudhury, Asim1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/estimation-gross-calorific-value-coals-using-artificial-neural-networks02613nas a2200265 4500008004100000022001400041245009200055210006900147260007300216300001200289490000800301520170400309653001402013653001302027653001502040653001702055653001302072100002502085700002202110700002202132700002202154700002302176700002602199856012202225 2007 eng d a0376-738800aGas sorption and transport in polyarylates: effect of substituent symmetry and polarity0 aGas sorption and transport in polyarylates effect of substituent aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcOCT a244-2510 v3033 a

The gas sorption properties of polyarylates based on bisphenol-A possessing symmetric/asymmetric substitution by nonpolar -CH3 group and symmetrically linked terephthalic acid were examined. The effects of substitution of polar bromine on terephalic acid moiety of polyarylate based on asymmetrically substituted bisphenol were also studied for physical, sorption and transport properties. The estimation of dual mode sorption parameters, solubility and diffusion coefficient revealed that nature of the substituent and substitution type plays a crucial role in depicting permeation properties. An asymmetric substitution by -CH3 group increased solubility coefficient of pure gases (N-2, O-2, CH4 and CO2) up to 27% and symmetric substitution increased the same up to 106%. This was coupled with 7-35% increase in solubility selectivity in both cases of substitution, which ascertained the usefulness of methyl group substitution in polyarylates based on terephalic acid. The pressure dependency of solubility coefficients and solubility selectivity was also investigated for these polyarylates. The sorption and transport properties of these polyarylates correlated well with physical properties of polyarylates and gases studied. The sorption/desorption kinetics of symmetrically substituted TMBisA-T was performed in order to deduce time dependent sorption behavior and to evaluate diffusivity coefficient. The apparent diffusion coefficients from sorption kinetics, desorption kinetics and from steady-states permeation-sorption were compared. The diffusion coefficients of CH4 and N-2 deduced by these methods correlated well with each other. (C) 2007 Elsevier B.V. All rights reserved.

10aDiffusion10akinetics10apermeation10apolyarylates10aSorption1 aKaradkar, Prasad, B.1 aKharul, Ulhas, K.1 aBhole, Yogesh, S.1 aBadhe, Yogesh, P.1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/gas-sorption-and-transport-polyarylates-effect-substituent-symmetry-and-polarity-000800nas a2200205 4500008004100000022001400041245012500055210006900180260006000249300000800309490000700317100002200324700001500346700001800361700002100379700002000400700002100420700002100441856013200462 2007 eng d a0018-819000aImprove polyethylene process control and product quality - using artificial intelligence-based sensors can improve costs0 aImprove polyethylene process control and product quality using a aBOX 2608, HOUSTON, TX 77252-2608 USAbGULF PUBL COcMAR a53+0 v861 aBadhe, Yogesh, P.1 aLonari, J.1 aTambe, S., S.1 aKulkarni, B., D.1 aValecha, N., K.1 aDeshmukh, S., V.1 aRavichandran, S. uhttps://library.ncl.res.in/content/improve-polyethylene-process-control-and-product-quality-using-artificial-intelligence-based01504nas a2200289 4500008004100000022001400041245008900055210006900144260008600213300001200299490000700311520051200318653003100830653002000861653002100881653001900902100002000921700001400941700002200955700001800977700001900995700001801014700002201032700001801054700002101072856012101093 2008 eng d a0882-750800aStudy of a laboratory-scale froth flotation process using artificial neural networks0 aStudy of a laboratoryscale froth flotation process using artific a325 CHESTNUT ST, SUITE 800, PHILADELPHIA, PA 19106 USAbTAYLOR & FRANCIS INCcDEC a130-1420 v293 a

A three-layer feed-forward artificial neural network (ANN) model, trained using the error back propagation algorithm, has been established to simulate the froth flotation process for the beneficiation of coal fines. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used to compute the parameters of the coal flotation process over wide-ranging experimental conditions.

10aback propagation algorithm10afroth flotation10alaboratory-scale10aneural network1 aKalyani, V., K.1 aPallavika1 aChaudhuri, Sanjay1 aCharan, Gouri1 aHaldar, D., D.1 aKamal, K., P.1 aBadhe, Yogesh, P.1 aTambe, S., S.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/study-laboratory-scale-froth-flotation-process-using-artificial-neural-networks-002425nas a2200229 4500008004100000022001400041245011700055210006900172260010200241300001200343490000700355520151100362653003501873653003301908653002501941100001701966700002001983700002202003700001802025700002102043856013102064 2010 eng d a0250-599100aSelf-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence0 aSelforganizing maps a tool to ascertain taxonomic relatedness ba aC V RAMAN AVENUE, SADASHIVANAGAR, P B \#8005, BANGALORE 560 080, INDIAbINDIAN ACAD SCIENCEScDEC a617-6270 v353 a

Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

10aCurvilinear component analysis10aPrincipal component analysis10aself-organizing maps1 aRaje, D., V.1 aPurohit, H., J.1 aBadhe, Yogesh, P.1 aTambe, S., S.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/self-organizing-maps-tool-ascertain-taxonomic-relatedness-based-features-derived-16s-rdna-002276nas a2200241 4500008004100000022001400041245010500055210006900160260009900229300001200328490000700340520134900347653002401696653002301720653002301743653002401766100002301790700002201813700001901835700002301854700002601877856013101903 2011 eng d a0016-236100aClassification of Indian power coals using K-means clustering and self organizing map neural network0 aClassification of Indian power coals using Kmeans clustering and aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcJAN a339-3470 v903 a

The present study reports results of the classification of Indian coals used in thermal power stations across India. For classifying these power coals a classical unsupervised clustering technique, namely ``K-Means Clustering'' and an artificial intelligence (AI) based nonlinear clustering formalism known as ``Self-Organizing Map (SOM)'' have been used for the first time. To conduct the said classification, five coal descriptor variables namely moisture, ash, volatile matter, carbon and gross calorific value (GCV) have been used. The classification results thereof indicate that Indian power coals from different geographical origins can be classified optimally into seven classes. It has also been found that the K-means and SOM based classification results exhibit similarity in close to 75% coal samples. Further, K-means and SOM based seven coal categories have been compared with as many grades of a commonly employed Useful Heat Value (UHV) based Indian non-coking coal grading system. Here, it was observed that a number of UHV-based grades exhibit similarity with the categories identified by the K-means and SOM methods. The classification of Indian power coals as provided here can be gainfully used in selecting application-specific coals as also in their grading and pricing. (C) 2010 Elsevier Ltd. All rights reserved.

10aCoal classification10aIndian power coals10aK-means clustering10aSelf-Organizing Map1 aPandit, Yogesh, P.1 aBadhe, Yogesh, P.1 aSharma, B., K.1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/classification-indian-power-coals-using-k-means-clustering-and-self-organizing-map-neural-002522nas a2200265 4500008004100000022001400041245011700055210006900172260009500241300001400336490000700350520150500357653003001862653003101892653001801923653002301941653002501964100001801989700002902007700002202036700002402058700002302082700002602105856012502131 2014 eng d a0263-876200aBiodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies0 aBiodegradable iron chelate for H2S abatement modeling and optimi a165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLANDbINST CHEMICAL ENGINEERScJUN a1119-11320 v923 a

A batch reactor process for the abatement of a common pollutant, namely, H2S using Fe3+-malic acid chelate (Fe3+-MA) catalyst has been developed. Further, process modeling and optimization was conducted in the three stages with a view to maximize the H2S conversion: (i) sensitivity analysis of process inputs was performed to select the most influential process operating variables and parameters, (ii) an artificial neural network (ANN)-based data-driven process model was developed using the influential process variables and parameters as model inputs, and H2S conversion (%) as the model output, and (iii) the input space of the ANN model was optimized using the artificial immune systems (AIS) formalism. The AIS is a recently proposed stochastic nonlinear search and optimization method based on the human biological immune system and has been introduced in this study for chemical process optimization. The AIS-based optimum process conditions have been compared with those obtained using the genetic algorithms (GA) formalism. The AIS-optimized process conditions leading to high (approximate to 97%) H2S conversion, were tested experimentally and the results obtained thereby show an excellent match with the AIS-maximized H2S conversion. It was also observed that the AIS required lesser number of generations and function evaluations to reach the convergence when compared with the GA. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

10aArtificial immune systems10aArtificial neural networks10aBatch reactor10aGenetic algorithms10aSensitivity analysis1 aHamid, Aashti1 aDeshpande, Aniruddha, S.1 aBadhe, Yogesh, P.1 aBarve, Prashant, P.1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/biodegradable-iron-chelate-h2s-abatement-modeling-and-optimization-using-artificial-0