Artificial Intelligence Treatment of Emissions from CFBC in Air and Oxygen-Enriched Conditions
Publication: Journal of Energy Engineering
Volume 142, Issue 1
Abstract
Because the complexity of sulfur capture and release during solid-fuel combustion in a circulating fluidized bed combustors (CFBC), especially in the oxygen-enriched combustion, has not been sufficiently recognized, the development of a simple model, which can correctly predict the emissions from such units over a wide range of operating conditions is of practical significance. The artificial neural network (ANN) approach is proposed in this paper, which may overcome the shortcomings of the experimental procedures and the programmed computing approach. The Ca:S molar ratio, oxygen concentration in inlet gas, excess oxygen, average riser temperature, mean diameter of the coal particles, average gas velocity in the riser, flue gas recycle ratio, and inlet gas pressure are taken into account by the model as the input parameters. The [8-3-7-1] ANN model with hyperbolic tangent sigmoid activation function was successfully applied to calculate the emissions from coal combustion in several CFB boilers operating under both air-fired and oxygen-enriched conditions.
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Acknowledgments
Financial support for this work by the Polish Government, as a part of Framework Project: Supercritical Coal-fired Power Units, is gratefully acknowledged.
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Journal of Energy Engineering
Volume 142 • Issue 1 • March 2016
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© 2015 American Society of Civil Engineers.
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Received: Nov 6, 2014
Accepted: Mar 17, 2015
Published online: Jun 1, 2015
Discussion open until: Nov 1, 2015
Published in print: Mar 1, 2016
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