Reverse microemulsion mediated sol-gel synthesis of lithium silicate nanoparticles under ambient conditions: Scope for CO2 sequestration - Khomane, Ramdas B.; Sharma, B. K.; Saha, S.; Kulkarni, B. D. (Chemical Engineering Science, 2006)
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.
Keywords: Adsorption; Carbon dioxide; Microemulsion; particle; separations; sol-gel; surfactant

Estimation of gross calorific value of coals using artificial neural networks - Patel, Shagufta U.; Kumar, B. Jeevan; Badhe, Yogesh P.; Sharma, B. K.; Saha, Sujan; Biswas, Subhasish; Chaudhury, Asim; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Fuel, 2007)
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.
Keywords: Artificial neural network; gross calorific value (GCV); proximate and ultimate analyses

Classification of Indian power coals using K-means clustering and self organizing map neural network - Pandit, Yogesh P.; Badhe, Yogesh P.; Sharma, B. K.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Fuel, 2011)
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.
Keywords: Coal classification; Indian power coals; K-means clustering; Self-Organizing Map