In this paper a neural network-based strategy is proposed for the estimation of the emissions in thermal power plants, fed with both oil and methane fuel. A detailed analysis based on a three-dimensional simulator of the combustion chamber has pointed out the local nature of the generation process, which takes place mainly in the burners’ zones. This fact has been suitably exploited in developing a compound estimation procedure, which makes use of the trained neural network together with a classical one-dimensional model of the chamber. Two different learning procedures have been investigated, both based on the external inputs to the burners and a suitable mean cell temperature, while using local and global flow rates as learning signals, respectively. The approach has been assessed with respect to both simulated and experimental data.
Estimation of Emissions in Thermal Power Plants Using Neural Networks
Contributed by the Power Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received by the Power Division, May 2000; final revision received by the ASME Headquarters January 2001. Editor: H. D. Nelson.
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Ferretti, G., and Piroddi, L. (January 1, 2001). "Estimation of Emissions in Thermal Power Plants Using Neural Networks ." ASME. J. Eng. Gas Turbines Power. April 2001; 123(2): 465–471. https://doi.org/10.1115/1.1367339
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