基于ARIMA-OSELM的火电厂SCR入口NOx浓度预测建模研究

doi:10.3969/j.issn.1000-1158.2023.09.21

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Abstract It is difficult to accurately measure the inlet NOx concentration in selective catalytic reduction flue gas denitration system (SCR) for the combustion system of thermal power plant due to complex operating conditions and large delay. Therefore, a method to predict NOx concentration at SCR inlet of thermal power plant based on ARIMA-OSELM neural network combination model was proposed. The method was compared from two combination perspectives: optimal weight and residual optimization. The above method was applied to the prediction of SCR inlet concentration in a thermal power plant. The results show that the combined model based on ARIMA-OSELM residual optimization has the highest prediction accuracy, and its effect is better than that of ARIMA-OSELM optimal weight combined prediction model and single ARIMA and OSELM neural network prediction model. The obtain evaluation indexes FMAPE, MRMSE and R2 are 0.190, 1.364 and 0.978, respectively.

Key words： metrology;NOx emission prediction combined prediction method optimal weight residual optimization ARIMA OSELM

Received: 30 January 2023 Published: 21 September 2023

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TB99

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	Articles by authors
	JIN Xiu-zhang
	CHEN Jia-zheng
	LI Yang-feng

Cite this article:

JIN Xiu-zhang,CHEN Jia-zheng,LI Yang-feng. SCR Inlet of Thermal Power Plant Based on ARIMA-OSELM Prediction of NOx Concentration[J]. Acta Metrologica Sinica, 2023, 44(9): 1458-1466.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.09.21 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I9/1458

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