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大型燃煤电站锅炉积灰的智能神经网络预测 认领

Intelligent neural network prediction of ash accumulation in boilers of large coal-fired power stations
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摘要 人工智能时代,人工神经网络拥有强大的计算能力、逼近能力、学习能力和动态分析能力,其中,以BP神经网络算法为代表的反向误差传播训练算法最常用,在燃煤电厂各个环节得到了广泛的应用。为了实现超临界燃煤锅炉受热面灰污的预测,本文采用基于BP神经网络预测锅炉低温受热面的清洁因子,从而实现安全、经济、稳定的燃烧。 In the era of artificial intelligence,artificial neural networks have powerful computing power,approximation power,learning power and dynamic analysis power.Among them,the back error propagation training algorithm represented by BP neural network algorithm is the most commonly used,and has been widely applied in all links of coal-fired power plants.In order to predict the fouling of the heating surface of the supercritical coal fired boiler,this paper USES BP neural network to predict the cleaning factor of the low-temperature heating surface of the boiler,so as to realize the safe,economical and stable combustion.
作者 邓云天 DENG Yun-tian(Shanghai Electric Power Station Environmental Protection Engineering Co.,Ltd.,Shanghai 200000,China)
出处 《电气传动自动化》 2020年第3期24-25,50,共3页 Electric Drive Automation
关键词 燃煤锅炉 省煤器 BP神经网络 清洁因子 coal-fired boiler economizer BP neural network clean factors
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