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- W1485394033 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 SO2 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 SO2 emissions from coal combustion in several CFB boilers operating under both air-fired and oxygen-enriched conditions." @default.
- W1485394033 created "2016-06-24" @default.
- W1485394033 creator A5072051618 @default.
- W1485394033 creator A5082322854 @default.
- W1485394033 date "2016-03-01" @default.
- W1485394033 modified "2023-10-18" @default.
- W1485394033 title "Artificial Intelligence Treatment of SO2 Emissions from CFBC in Air and Oxygen-Enriched Conditions" @default.
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- W1485394033 doi "https://doi.org/10.1061/(asce)ey.1943-7897.0000280" @default.
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