基于特征优化和BSO-RBF神经网络的NOx浓度预测模型

doi:10.3969/j.issn.1000-1158.2024.02.20

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Abstract In the process of thermal power generation, the operation condition of combustion system is complicated and the delay is large, which makes it difficult to accurately measure the inlet NOx mass concentration in the selective catalytic reduction (SCR) flue gas denitration system. To solve this problem, a prediction model based on feature optimization and radial basis (RBF) neural network is proposed. Firstly, the variable after feature optimization is taken as the final input variable of the model. Secondly, the beetle swarm optimization (BSO) is used to optimize the neural network hyperparameters. Finally, a prediction model of inlet NOx concentration is established. The results show that the predictive results of the optimized variables are better than those of the original variables. After feature optimization and timely delay, the SRMSE of the model decreased by 44.5%, and the R2 increased by 2.3%. The neural network hyperparameters determined by BSO also improved the accuracy of the model.

Key words： NOx concentration prediction feature optimization beetle swarm optimization algorithm RBF neural network

Received: 12 July 2023 Published: 21 February 2024

PACS:	TB99
	TB973

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	Articles by authors
	ZHANG Guoxing
	WANG Shipeng

Cite this article:

ZHANG Guoxing,WANG Shipeng. NOx Concentration Prediction Model Based on Feature Optimization and BSO-RBF Neural Network[J]. Acta Metrologica Sinica, 2024, 45(2): 285-293.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.02.20 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I2/285

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