02576nas a2200277   4500008004100000022001400041245008200055210006900137260009900206300001200305490000700317520157600324653003001900653003201930653003601962100002401998700001802022700002202040700001902062700001602081700002202097700002002119700002302139700002602162856011002188       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<p>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.</p>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.  uhttp://library.ncl.res.in/content/estimation-gross-calorific-value-coals-using-artificial-neural-networks02361nas a2200277   4500008004100000022001400041245014500055210006900200260008000269300001200349490000700361520130400368653003001672653001801702653002201720653002301742653003301765653001601798100002101814700002801835700002101863700002201884700002101906700002601927856013001953       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<p>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.</p>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.  uhttp://library.ncl.res.in/content/media-optimization-biosurfactant-production-rhodococcus-erythropolis-mtcc-2794-artificial-0