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<p>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.</p>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<p><span style="color: rgb(51, 51, 51); font-family: arial, helvetica, sans-serif; font-size: 13px; line-height: 22px; background-color: rgb(248, 248, 248);">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.</span></p>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-200800nas 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<p>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.</p>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<p>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.</p>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-001871nas a2200229   4500008004100000022001400041245017100055210006900226260007400295300001200369490000800381520094500389653002401334653001301358653003101371653002401402100002901426700001901455700001801474700001801492856013101510       2013                        eng d  a1388-615000aPhotopolymerization of bis-aromatic and alicyclic based solid urethane acrylate macromonomer in the presence of large excess of reactive diluent Kinetics and modeling0 aPhotopolymerization of bisaromatic and alicyclic based solid ure  aVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDSbSPRINGERcMAY  a805-8130 v1123 a<p>A solid urethane acrylate macromonomer with bis-aromatic as well as alicyclic moieties was synthesized and the kinetics of photopolymerization reactions were studied in the presence of varying concentration of photoinitiator and large excess of reactive diluent using photo DSC. The studies show that the rate of maximum polymerization was found to increase with increase in concentration of photoinitiator while a decrease was observed by an increase in temperature. The final conversion showed a decrease at highest isothermal condition due to vitrification. Estimation of kinetic parameters including applicability of autocatalytic and modified autocatalytic models were investigated by nonlinear regression. It was observed that the modified models gave a better fit with the experimental data and kinetic parameters showed a decrease with increase in temperature and an increase with increase in concentration of photoinitiator.</p>10aAutocatalytic model10akinetics10aLevenberg-Marquardt method10aPhotopolymerization1 aHarikrishna, Reghunathan1 aPonrathnam, S.1 aRajan, C., R.1 aTambe, S., S.  uhttps://library.ncl.res.in/content/photopolymerization-bis-aromatic-and-alicyclic-based-solid-urethane-acrylate-macromonomer-002302nas a2200253   4500008004100000022001400041245009500055210006900150260011100219300001400330490000700344520138000351653001801731653002901749653002401778653000801802653000801810100001501818700001601833700003301849700001801882700002101900856012701921       2014                        eng d  a1570-180800aPharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method0 aPharmacokinetic modeling of caco2 cell permeability using geneti  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcNOV  a1112-11180 v113 a<p>An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.</p>10aADME modeling10aCaco-2 cell permeability10agenetic programming10aMLP10aSVR1 aVyas, Renu1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, S., S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/pharmacokinetic-modeling-caco-2-cell-permeability-using-genetic-programming-gp-method-002882nas a2200253   4500008004100000022001400041245010300055210006900158260005700227300001200284490000600296520197700302653002802279653003102307653001802338653002402356653002502380653002602405100002302431700001502454700001802469700001802487856012302505       2014                        eng d  a1939-123400aPrediction of higher heating value of solid biomass fuels using artificial intelligence formalisms0 aPrediction of higher heating value of solid biomass fuels using   a233 SPRING ST, NEW YORK, NY 10013 USAbSPRINGERcJUN  a681-6920 v73 a<p>The higher heating value (HHV) is an important property defining the energy content of biomass fuels. A number of proximate and/or ultimate analysis based predominantly linear correlations have been proposed for predicting the HHV of biomass fuels. A scrutiny of the relationships between the constituents of the proximate and ultimate analyses and the corresponding HHVs suggests that all relationships are not linear and thus nonlinear models may be more appropriate. Accordingly, a novel artificial intelligence (AI) formalism, namely genetic programming (GP) has been employed for the first time for developing two biomass HHV prediction models, respectively using the constituents of the proximate and ultimate analyses as the model inputs. The prediction and generalization performance of these models was compared rigorously with the corresponding multilayer perceptron (MLP) neural network based as also currently available high-performing linear and nonlinear HHV models. This comparison reveals that the HHV prediction performance of the GP and MLP models is consistently better than that of their existing linear and/or nonlinear counterparts. Specifically, the GP- and MLP-based models exhibit an excellent overall prediction accuracy and generalization performance with high (&gt; 0.95) magnitudes of the coefficient of correlation and low (&lt; 4.5 %) magnitudes of mean absolute percentage error in respect of the experimental and model-predicted HHVs. It is also found that the proximate analysis-based GP model has outperformed all the existing high-performing linear biomass HHV prediction models. In the case of ultimate analysis-based HHV models, the MLP model has exhibited best prediction accuracy and generalization performance when compared with the existing linear and nonlinear models. The AI-based models introduced in this paper due to their excellent performance have the potential to replace the existing biomass HHV prediction models.</p>10aArtificial intelligence10aArtificial neural networks10aBiomass fuels10agenetic programming10aHigher heating value10aMultilayer perceptron1 aGhugare, Suhas, B.1 aTiwary, S.1 aElangovan, V.1 aTambe, S., S.  uhttps://library.ncl.res.in/content/prediction-higher-heating-value-solid-biomass-fuels-using-artificial-intelligence-002279nas a2200217   4500008004100000022001400041245011600055210006900171260007300240300001200313490000800325520144300333653002401776653001301800653003101813653002401844100002901868700001901897700001801916856012701934       2014                        eng d  a0168-583X00aReaction kinetics and modeling of photoinitiated cationic polymerization of an alicyclic based diglycidyl ether0 aReaction kinetics and modeling of photoinitiated cationic polyme  aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcJAN  a263-2680 v3183 a<p>Photoinitiated cationic polymerization of cycloaliphatic diepoxides had received tremendous attention, while studies with lesser polymerizable diglycidyl ethers are comparatively less reported. The present work deals with the photoinitiated cationic polymerization of cyclohexane dimethanol diglycidyl ether followed by estimation of kinetic parameters. The effects of concentration of photoinitiator and temperature on curing performance were studied using photo differential scanning calorimeter or photo DSC with polychromatic radiation. It was observed that the rate of polymerization as well as ultimate conversion increased with increasing concentration of photoinitiator and temperature. The influences of gelation as well as diffusional restrictions have remarkable effect on cure performance. The kinetic parameters as per autocatalytic kinetic model were studied by Levenberg-Marquardt nonlinear regression method instead of conventional linear method for obtaining more accurate values of apparent rate constant. It was observed that the model fits with data from initial stages to almost towards the end of the reaction. The activation energy was found to be higher than the values reported for more reactive cycloaliphatic diepoxides. The value of pre-exponential factor increased with increase in activation energy showing influence of gelation at early stages of reaction. (C) 2013 Elsevier B.V. All rights reserved.</p>10aAutocatalytic model10akinetics10aLevenberg-Marquardt method10aPhotopolymerization1 aHarikrishna, Reghunathan1 aPonrathnam, S.1 aTambe, S., S.  uhttps://library.ncl.res.in/content/reaction-kinetics-and-modeling-photoinitiated-cationic-polymerization-alicyclic-based-001740nas a2200169   4500008004100000245008500041210006900126260000800195300001400203490000700217520115600224100001901380700001901399700001801418700001901436856011501455       2016                        eng d00aAdsorption of strontium (II) metal ions using phosphonate-functionalized polymer0 aAdsorption of strontium II metal ions using phosphonatefunctiona  cOCT  a1541-15560 v393 aDiethyl[3-(methoxydimethylsilyl)propyl]phosphonate (DMPP) polymer was synthesized for the strontium (II) metal ion recovery using diethylallylphosphonate as staring material. Diethylallylphosphonate was reacted with poly(methylhydro)siloxane (MW 1900-2000 g mol (-1) ) in the presence of Speier's catalyst. The synthesized monomer was characterized by IR, (1) H NMR, (1 3) C NMR and FT-IR spectroscopy techniques, and the synthesized polymers were characterized by IR and NMR spectroscopy, differential scanning calorimetry, thermogravimetric analysis and solubility. The synthesized polymer was used for sequestering strontium metal from the aqueous solution. The metal binding was examined by the energy dispersive spectroscopy and scanning electron microscopy for the adsorbed Sr(II). Batch adsorption studies were performed by varying three parameters, namely initial pH, adsorbent dose and the contact time. The reaction kinetics was determined by the Langmuir, Freundlich, and pseudo-first- and second-order models. Results of this study indicate that the synthesized polymer DMPP has been effective in removing Sr(II) from the aqueous solution.1 aBhosle, S., M.1 aPonrathnam, S.1 aTambe, S., S.1 aChavan, N., N.  uhttps://library.ncl.res.in/content/adsorption-strontium-ii-metal-ions-using-phosphonate-functionalized-polymer02393nas a2200193   4500008004100000245017100041210006900212260000800281300001400289490000600303520165900309100002101968700001901989700001402008700001902022700001902041700001802060856012102078       2016                        eng d00aRemoval of arsenite [As(III)] and arsenate [As(V)] ions from wastewater using TFA and TAFA resins: computational intelligence based reaction modeling and optimization0 aRemoval of arsenite AsIII and arsenate AsV ions from wastewater   cDEC  a4275-42860 v43 aBeing significantly toxic, removal of arsenic forms an important part of the drinking- and waste-water treatment. Tannin is a polyphenol-rich substrate that efficiently and adsorptively binds to the multivalent metal ions. In this study, tannin-formaldehyde (TFA) and tannin-aniline-formaldehyde (TAFA) resins were synthesized and employed successfully for an adsorptive removal of arsenite [As(III)] and arsenate [As(V)] ions from the contaminated water. Next, a computational intelligence (CI) based hybrid strategy was used to model and optimize the resin-based adsorption of As(III) and As(V) ions for securing optimal reaction conditions. This strategy first uses an exclusively reaction data driven modeling strategy, namely, genetic programming (GP) to predict the extent (%) of As(III)/As(V) adsorbed on TFA and TAFA resins. Next, the input space of the GP-based models consisting of the reaction condition variables/parameters was optimized using genetic algorithm (GA) method; the objective of this optimization was to maximize the adsorption of As(III) and As(V) ions on the two resins. Finally, the sets of optimal reaction conditions provided by GP-GA hybrid method were verified experimentally the results of which indicate that the optimized conditions have lead to 0.3% and 1.3% increase in the adsorption of As(III) and As(V) ions on TFA resin. More significantly, the optimized conditions have increased the adsorption of As(III) and As(V) on TAFA resin by 3.02% and 12.77%, respectively. The GP-GA based strategy introduced here can be gainfully utilized for modeling and optimization of similar type of contaminant-removal processes. 1 aPatil-Shinde, V.1 aMulani, K., B.1 aDonde, K.1 aChavan, N., N.1 aPonrathnam, S.1 aTambe, S., S.  uhttps://library.ncl.res.in/content/removal-arsenite-asiii-and-arsenate-asv-ions-wastewater-using-tfa-and-tafa-resins00633nas a2200169   4500008004100000022001400041245010000055210006900155260000800224490000700232100002200239700001800261700001900279700001800298700002100316856012600337       2017                        eng d  a2016-021000aGenetic programming based drag model with improved prediction accuracy for fluidization systems0 aGenetic programming based drag model with improved prediction ac  cAPR0 v151 aSonolikar, R., R.1 aPatil, M., P.1 aMankar, R., B.1 aTambe, S., S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/genetic-programming-based-drag-model-improved-prediction-accuracy-fluidization-systems01714nas a2200193   4500008004100000022001400041245008300055210006900138260000900207300001400216490000800230520107100238100002201309700001801331700001901349700001801368700002101386856011301407       2018                        eng d  a0011-389100a Bubble size prediction in gas-solid fluidized beds using genetic programming 0 aBubble size prediction in gassolid fluidized beds using genetic   cNOV   a1904-19120 v1153 aThe hydrodynamics of a gas-solid fluidized bed (FB) is affected by the bubble diameter, which in turn strongly influences the performance of a fluidized bed reactor (FBR). Thus, determining the bubble diameter accurately is of crucial importance in the design and operation of an FBR. Various equations are available for calculating the bubble diameter in an FBR. It has been found in this study that these models show a large variation while predicting the experimentally measured bubble diameters. Accordingly, the present study proposes a new equation for computing the bubble diameter in a fluidized bed. This equation has been developed using an efficient, yet infrequently employed computational intelligence (CI)-based data-driven modelling method termed genetic programming (GP). The prediction and generalization performance of the GP-based equation has been compared with that of a number of currently available equations for computing the bubble diameter in a fluidized bed and the results obtained show a good performance by the newly developed equation.1 aSonolikar, R., R.1 aPatil, M., P.1 aMankar, R., B.1 aTambe, S., S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/bubble-size-prediction-gas-solid-fluidized-beds-using-genetic-programming