基于改进樽海鞘算法的锅炉NOX排放模型优化研究

doi:10.3969/j.issn.1000-1158.2020.09.18

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Abstract In order to establish an efficient prediction model for NOx emission concentration, a 330 MW pulverized coal boiler was used as the research object and the adaptive salp swarm algorithm (ASSA) was used to optimize the fast learning network (FLN) to set up a prediction model. Firstly, the performance of ASSA was detected by 8 benchmark functions and compared with the other three algorithms. The results show that the convergence speed of ASSA algorithm is faster and the optimization result is better. In addition, the model was compared with the fast learning network, which was optimized by the differential evolution algorithm, the particle swarm optimization algorithm and the salp swarm algorithm. The results show that the ASSA-FLN model has better prediction accuracy and generalization ability, and can effectively and accurately predict the NOx emission of pulverized coal fired boilers.

Key words： metrology NOx emission concentration salp swarm algorithm fast learning network

Received: 14 September 2018 Published: 28 August 2020

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TB99

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	Articles by authors
	NIU Pei-feng
	MIAO Kong-hao
	SHANG Shi-xin
	CHANG Ling-fang
	ZHANG Xian-chen

Cite this article:

NIU Pei-feng,MIAO Kong-hao,SHANG Shi-xin, et al. Optimization Research of Boiler NOx Emission Model Based on Improved Salp Swarm Algorithm[J]. Acta Metrologica Sinica, 2020, 41(9): 1146-1151.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.09.18 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I9/1146

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