基于变量选择的电站燃煤锅炉NOx排放浓度预估

doi:10.3969/j.issn.1000-1158.2023.10.15

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Abstract To study the measurement delay of NOx emission concentration of coal-fired boilers in power stations, a prediction model of NOx emission concentration of coal-fired boilers in power stations based on mutual information (MI) and long short-term memory neural network (LSTM) was proposed. Firstly, the delay time between the candidate input variable and the output variable NOx concentration was calculated using mutual information, and the MRMR algorithm was introduced to screen out the optimal feature subset, and the optimal feature subset was used as the input of LSTM model. The prediction model of NOx emission concentration of boiler is established. The simulation results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed model are 4.626mg/m³ and 3.836mg/m³, respectively. Compared with the LSTM model without variable selection and delay consideration, the prediction accuracy is significantly improved.

Key words： metrology NOx emission concentration coal-fired boilers variable selection mutual information LSTM network

Received: 31 May 2022 Published: 10 October 2023

PACS:	TB99
	TB973

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	Articles by authors
	WANG Long-xian
	ZHAO Wen-jie

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WANG Long-xian,ZHAO Wen-jie. Estimation of NOx Emission Concentration from Coal-fired Boilers of Power Stations Based on Variable Selection[J]. Acta Metrologica Sinica, 2023, 44(10): 1590-1596.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.10.15 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I10/1590

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