@article { ISI:000315616400005,
	title = {Hydrogen generation with CO2 utilization: a solvay cluster study},
	journal = {International Journal of Hydrogen Energy},
	volume = {38},
	number = {6},
	year = {2013},
	month = {FEB},
	pages = {2624-2633},
	publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
	address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
	abstract = {<p>The fuel cell economy is yet to start research programs in hydrogen generation with CO2 utilization for hydrocarbon reforming processes used in fuel processor applications. A simple thermodynamic study using solvay clusters was done to investigate the feasibility of using the carbon species produced in the steam methane reforming process to produce value added chemicals. The results of this study are highly encouraging to start process development of closed systems of hydrogen generation with CO2 conversion to acetic acid/acrylic acid making easier the commercialization of fuel cells and hydrogen energy. Such studies can be specifically carried out for different fuel processor systems. Copyright (C) 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.</p>},
	keywords = {CO2 utilization, Fuel processing, Gibbs energy, Solvay clusters, Steam reforming},
	issn = {0360-3199},
	doi = {10.1016/j.ijhydene.2012.11.129},
	author = {Kale, Ganesh R. and Kulkarni, Bhaskar D.}
}
@article { ISI:000379250900005,
	title = {High ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms},
	journal = {Chemical Engineering Communications},
	volume = {203},
	number = {8},
	year = {2016},
	month = {JAN},
	pages = {1029-1044},
	publisher = {TAYLOR \& FRANCIS INC},
	address = {530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA},
	abstract = {<p>The coal gasification is a cleaner and more efficient process than the coal combustion. Although high ash coals are commonly utilized in the energy generation, systematic gasification kinetic studies using chars derived from these coals are scarce. Accordingly, this paper reports the development of the data-driven models for the gasification of chars derived from the high ash coals. Specifically, the models predict two important gasification performance parameters, viz. gasification rate constant and reactivity index. These models have been constructed using three computational intelligence (CI) methods, namely genetic programming (GP), multilayer perceptron (MLP) neural network (NN), and support vector regression (SVR). The inputs to the CI-based models consist of seven parameters representing the gasification reaction conditions and properties of high ash coals and chars. The data used in the modeling were collected by performing extensive gasification experiments in the CO2 atmosphere in a thermo-gravimetric analyzer (TGA) using char samples derived from the Indian coals containing high ash content. Values of the two gasification performance parameters were obtained by fitting the experimental data to the shrinking unreacted core (SUC) model. It has been observed that all the CI-based models possess an excellent prediction accuracy and generalization capability. Accordingly, these models can be gainfully employed in the design and operation of the fixed and fluidized bed gasifiers using high ash coals.</p>},
	keywords = {Char gasification kinetic modeling, Data-driven modeling, genetic programming, Multilayer perceptron neural network, support vector regression, Thermo-gravimetric analyzer},
	issn = {0098-6445},
	doi = {10.1080/00986445.2015.1135795},
	author = {Patil-Shinde, Veena and Saha, Sujan and Sharma, Bijay K. and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.}
}
