Forecasting the NOx Emissions from Utility Boilers Based on Independent Component Analysis

doi:10.11930/j.issn.1004-9649.2013.9.39.4

Abstract

Abstract: There are many high dimensional input values in the modeling process for low NO_x combustion property of coal-fired utility boilers, Moreover, processing such a large amount of sample data may slow down the calculations and reduce forecasting accuracy. To solve these problems, the independent component analysis(ICA) is applied to data pretreatment and then the back propagation(BP) neural network model based on fastICA algorithm is established in this paper. The ICA-BP model is used to predict the NO_x emission from a 220-MW thermal power unit. The results indicated that the neural network model based on fastICA algorithm outperforms the one without data preprocessing. The relative error of the ICA-BP model was only about 2.5% between the calculation and the measured results, in which the ICA method is verified to be an effective tool for data pretreatment in system modeling in the aspect of lowering the dimensions while more original data characteristic information still retained simultaneously is.

Key words: independent component analysis, neural network, utility boiler, low NOx combustion, emission prediction

CLC Number:

TK16

SUN Bao-min, XIN Jing, YANG Bin, WANG Lan-zhong, WANG Chong. Forecasting the NO_x Emissions from Utility Boilers Based on Independent Component Analysis[J]. Electric Power, 2013, 46(9): 39-43.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2013.9.39.4

https://www.electricpower.com.cn/EN/Y2013/V46/I9/39

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Forecasting the NO_x Emissions from Utility Boilers Based on Independent Component Analysis

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