@article { ISI:000284150200042, title = {Classification of Indian power coals using K-means clustering and self organizing map neural network}, journal = {Fuel}, volume = {90}, number = {1}, year = {2011}, month = {JAN}, pages = {339-347}, publisher = {ELSEVIER SCI LTD}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}, abstract = {

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 {\textquoteleft}{\textquoteleft}K-Means Clustering{\textquoteright}{\textquoteright} and an artificial intelligence (AI) based nonlinear clustering formalism known as {\textquoteleft}{\textquoteleft}Self-Organizing Map (SOM){\textquoteright}{\textquoteright} 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}, issn = {0016-2361}, doi = {10.1016/j.fuel.2010.09.012}, author = {Pandit, Yogesh P. and Badhe, Yogesh P. and Sharma, B. K. and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.} } @article {44432, title = {Co-gasification of high ash coal{\textendash}biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling}, journal = {Waste and Biomass Valorization}, volume = {11}, year = {2018}, month = {JUN}, pages = {1-19}, type = {Article}, abstract = {Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.}, keywords = {Artificial neural networks, Co-gasification, Computational intelligence, Fluidized bed gasifier, genetic programming, support vector regression}, issn = {1877-2641}, doi = {10.1007/s12649-018-0378-7}, author = {Tiwary, Shishir and Ghugare, Suhas B. and Chavan, Prakash D. and Saha, Sujan and Datta, Sudipta and Sahu, Gajanan and Tambe, Sanjeev S.} } @article {47151, title = {Co-gasification of high ash coal{\textendash}biomass blends in a fluidized bed gasifier: }, journal = {Waste and Biomass Valorization }, volume = {11}, year = {2020}, month = {JAN}, pages = {323{\textendash}341}, type = {Article}, abstract = {

Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the\ product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three\ computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.

}, author = {Tiwary, Shishir and Ghugare, Suhas B. and Chavan, Prakash D. and Saha, Sujan and Datta, Sudipta and Sahu, Gajanan and Tambe, Sanjeev S.} }