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Modelling and measurement accuracy enhancement of flue gas flow using neural networks

Abstract

This paper discusses the modeling of the flue gas flow in industrial ducts and stacks using artificial neural networks (ANN's). Based upon the individual velocity and other operating conditions, an ANN model has been developed for the measurement of the volume flow rate. The model has been validated by the experiment using a case-study power plant. The results have shown that the model can largely compensate for the nonrepresentativeness of a sampling location and, as a result, the measurement accuracy of the flue gas flow can be significantly improved

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