01620nas a2200265 4500008004100000022001400041245014100055210006900196260010600265300001400371490000700385520065000392653001501042653001901057653001801076653001301094653001601107653001201123653001501135100002401150700001901174700001301193700002101206856012701227 2006 eng d a0009-250900aReverse microemulsion mediated sol-gel synthesis of lithium silicate nanoparticles under ambient conditions: Scope for CO2 sequestration0 aReverse microemulsion mediated solgel synthesis of lithium silic aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcMAY a3415-34180 v613 a

We report on the synthesis of nanocrystalline lithium silicate by coupling of sol-gel method in reverse microemulsion. The sample calcined at 800 degrees C gives pure phase lithium metasilicate nanocrystallites. X-ray diffraction and transmission electron microscopy confirmed the formation of nanocrystalline lithium silicate particles with a narrow size distribution. The nanoparticle prepared in the microemulsion shows enhanced CO2 sorption capacity and shorter retention times at higher temperature (similar to 131 ml/g at STP at 610 degrees C which are better than the best known results. (c) 2005 Elsevier Ltd. All rights reserved.

10aAdsorption10aCarbon dioxide10aMicroemulsion10aparticle10aseparations10asol-gel10asurfactant1 aKhomane, Ramdas, B.1 aSharma, B., K.1 aSaha, S.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/reverse-microemulsion-mediated-sol-gel-synthesis-lithium-silicate-nanoparticles-under-002577nas 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-networks02276nas 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-0