Development of data-driven models for fluidized-bed coal gasification process

Coal gasification is one of the most viable and practical clean coal technologies for power generation. In this technology, the low calorific value fuel (LCVF) gas containing mainly carbon monoxide (CO), hydrogen (H2) and carbon dioxide (CO2) are obtained by blowing a mixture of air and steam throug...

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Bibliographic Details
Published in:	Fuel (Guildford) Vol. 93; pp. 44 - 51
Main Authors:	Chavan, P.D., Sharma, T., Mall, B.K., Rajurkar, B.D., Tambe, S.S., Sharma, B.K., Kulkarni, B.D.
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01-03-2012 Elsevier
Subjects:	ANN modeling Applied sciences Calorific value Coal Coal gasification Coal mines Energy Energy. Thermal use of fuels Exact sciences and technology Fluidized bed gasifier Fuels Gasification Input sensitivity Learning theory Mathematical models Neural networks Asia India Input sensitivity ANN modeling Coal gasification Fluidized bed gasifier Gaseous fuel Hydrogen Pollution control Carbon dioxide Modeling Accuracy Ash content Coal Carbon monoxide Power production Pollution prevention Regression Fuel gas Neural network Air blowing Water vapor Volatiles Steady state Mineral matter Calorific value Performance
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Summary:	Coal gasification is one of the most viable and practical clean coal technologies for power generation. In this technology, the low calorific value fuel (LCVF) gas containing mainly carbon monoxide (CO), hydrogen (H2) and carbon dioxide (CO2) are obtained by blowing a mixture of air and steam through a bed of coal at atmospheric or elevated pressures. In this paper, coal gasification data from 18 fluidized bed gasifiers (FBGs) operating at a steady-state and located in India and other countries have been used to develop two types of exclusively data-driven steady-state FBG models. Specifically, multivariate regression (MVR) and multilayer Artificial Neural Network (ANN) strategies have been employed for the prediction of gas produced rate and heating value of the product gas. Both types of models use six inputs describing coal properties and gasification process parameters namely fixed carbon, volatile matter, mineral matter, air feed per kg of coal, steam feed per kg of coal and temperature. The output prediction accuracy results of the developed models indicate that the ANN-based models outperform the corresponding MVR models. The models presented here can be gainfully employed for evaluating steady-state performance of an FBG as well as the thermal potential of a coal prior to its usage for the generation of the LCVF gas. These models would also assist in the design of a gasifier for coals with varying ash content.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0016-2361 1873-7153
DOI:	10.1016/j.fuel.2011.11.039