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- Title
基于线性回归与BP神经网络的火电厂燃煤碳排放计算研究.
- Authors
龚广京; 周 光; 郑 涛; 陈时熠
- Abstract
In view of the general lack of coal ultimate analysis data in coal-fired power plants, based on more than 3 000 pieces of quality data in China′s commercial coal quality database, a linear regression model, a BP neural network model and a sparrow search algorithm (SSA) optimized BP neural network model were established. The coal proximate analysis data were fitted in three models to predict the carbon content of the coal ultimate analysis, which was further applied to calculate the carbon emission of coal combustion from stock side, and the relative errors of the carbon content of the coal ultimate analysis predicted by three models were 8.40%, 2.51% and 1.30%, respectively. A 1 000 MW power plant unit under four typical load conditions of stationary load, fluctuating load, load up and load down was selected to calculate the continuous coal-fired carbon emissions through the proposed three models from stock side, and the carbon emission value was compared with that detected from flue gas side of power plant. The results show that the proposed linear regression, BP neural network and SSA-BP neural network models can predict the carbon content of coal ultimate analysis well. The root mean square error (RMSE) of carbon emissions of coal combustion obtained from flue gas side under three working conditions of low, medium and high stationary loads are 0.35, 0.08, 0.07 and 0.87, 0.37, 0.09 as well as 0.23, 0.19, 0.17. The RMSEs of computational values of three models under three working conditions of load up, load down and load fluctuation are 1.00, 0.84, 0.71 and 1.43, 1.24, 0.73 as well as 1.33, 1.15, 0.93. Taking a typical working day of a power plant as an example, the relative deviations between the total daily carbon emissions calculated by three models and the carbon emissions obtained by flue gas detection method are 12.28%, 5.52% and 0.22%, respectively. SSA-BP neural network model has the smallest deviation of the coal quality prediction and carbon emission calculation result from the measured values on the flue gas side.
- Publication
Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng, 2024, Vol 39, Issue 3, p73
- ISSN
1001-2060
- Publication type
Article
- DOI
10.16146/j.cnki.rndlge.2024.03.10