Journal
FUEL PROCESSING TECHNOLOGY
Volume 225, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.fuproc.2021.107024
Keywords
Attrition; Attrition rate coefficient; Fluidized bed; Artificial neural network
Funding
- Korea Southern Power Company (KOSPO)
- Korea Ministry of Environment (MOE) as MOE's R&D program on environmental technology development [2020003060005]
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An artificial neural network model was developed to estimate the attrition rate coefficient Kaf of a circulating fluidized bed combustor, and field tests burning different coal blending ratios verified the validity of the model.
Attrition of ash has a significant effect on the performance of a circulating fluidized bed (CFB) combustor, and the attrition rate coefficient Kaf is widely used to analyze the mass balance of the CFB combustor. In this study, a four-layer artificial neural network (ANN) model is developed to estimate the value of Kaf according to the chemical components of the ash based on a training database consisting of 40 sets of samples. An optimum structure comprising two hidden layers with ten neurons in each layer is adopted, and the mean square error for training and validation stages is reduced to 0.00175 and 0.13842, respectively. To verify the validity of the model, field tests are conducted in a large-scale CFB boiler by burning two types of coal with different blending ratios. The Kaf of the two kinds of coal is estimated using the trained ANN model. The ratio of the coal ash discharged via fly ash to the total coal ash decreases, while the Sauter mean diameter of the circulating material increases with an increase in the blending ratio of the coal with a smaller value of estimated Kaf; this exhibits good validity of the model.
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