Elman neural network in the soft sensor modelling for the unburned carbon in fly ash from utility boilers

Xiu-Zhang Jin, Lin Li
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引用次数: 2

Abstract

Unburned carbon in fly ash is an important parameter affecting combustion efficiency of coal-fired boiler. In view of the deficiency of feed-forward neural network soft sensor modeling on unburned carbon in fly ash from the power plant, in this paper, we make use of recurrent Elman neural network to realize dynamic modeling of the boiler combustion process. A set of operating data from a 300MW power plant boiler is used here to train and validate the soft sensor model. Then this is compared with the results of BP network. The results after comparing show that Elman network can better achieve soft sensor modeling for unburned carbon in fly ash.
Elman神经网络在电厂锅炉飞灰未燃碳软测量建模中的应用
粉煤灰中未燃碳是影响燃煤锅炉燃烧效率的重要参数。针对前馈神经网络软传感器对电厂飞灰中未燃碳建模的不足,本文利用递归Elman神经网络实现锅炉燃烧过程的动态建模。本文采用300MW电厂锅炉运行数据对软测量模型进行训练和验证。然后与BP网络的结果进行比较。对比结果表明,Elman网络能较好地实现飞灰中未燃碳的软测量建模。
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