<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Desai, Kiran M.</style></author><author><style face="normal" font="default" size="100%">Akolkar, S. K.</style></author><author><style face="normal" font="default" size="100%">Badhe, Yogesh P.</style></author><author><style face="normal" font="default" size="100%">Tambe, S. S.</style></author><author><style face="normal" font="default" size="100%">Lele, S. S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques</style></title><secondary-title><style face="normal" font="default" size="100%">Process Biochemistry</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial neural network</style></keyword><keyword><style  face="normal" font="default" size="100%">exopolysaccharide</style></keyword><keyword><style  face="normal" font="default" size="100%">Fermentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Lactobacillus plantarum</style></keyword><keyword><style  face="normal" font="default" size="100%">Media optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Plackett-Burman</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">8</style></number><publisher><style face="normal" font="default" size="100%">ELSEVIER SCI LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND</style></pub-location><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">1842-1848</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">2.528</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patel, Shagufta U.</style></author><author><style face="normal" font="default" size="100%">Kumar, B. Jeevan</style></author><author><style face="normal" font="default" size="100%">Badhe, Yogesh P.</style></author><author><style face="normal" font="default" size="100%">Sharma, B. K.</style></author><author><style face="normal" font="default" size="100%">Saha, Sujan</style></author><author><style face="normal" font="default" size="100%">Biswas, Subhasish</style></author><author><style face="normal" font="default" size="100%">Chaudhury, Asim</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of gross calorific value of coals using artificial neural networks</style></title><secondary-title><style face="normal" font="default" size="100%">Fuel</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial neural network</style></keyword><keyword><style  face="normal" font="default" size="100%">gross calorific value (GCV)</style></keyword><keyword><style  face="normal" font="default" size="100%">proximate and ultimate analyses</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">ELSEVIER SCI LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND</style></pub-location><volume><style face="normal" font="default" size="100%">86</style></volume><pages><style face="normal" font="default" size="100%">334-344</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">3.611</style></custom4></record></records></xml>