基于改进最优觅食算法的锅炉NO<sub><em>x</em></sub>排放预测研究

doi:10.3969/j.issn.1000-1158.2020.07.19

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Abstract An improved optimal foraging algorithm is proposed, the update direction of adaptive inertia weight and global optimal solution is introduced in the optimal foraging algorithm, at the same time, the mechanism of phase space search is introduced. The improved optimal foraging algorithm (POFA) optimized by extreme learning machine (ELM) constructs an improved extreme learning machine model (POFA-ELM) and uses the POFA-ELM model to model the boiler NO_x emission characteristics.Compared with ELM and differential evolution algorithm, particle swarm optimization, artificial bee colony algorithm, and basic optimal foraging algorithm optimized ELM model, the results show that the POFA-ELM model has better prediction accuracy and stronger generalization ability, which can predict the NOx emission quality concentration more accurately.

Key words： metrology NO_x emission characteristics optimal foraging algorithm extreme learning machine coal-fired boiler

Received: 25 September 2018 Published: 29 June 2020

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TB99

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	NIU Pei-feng
	PENG Peng

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NIU Pei-feng,PENG Peng. Prediction of NO_x Emission of Boiler Based on Improved Optimal Foraging Algorithm[J]. Acta Metrologica Sinica, 2020, 41(7): 879-885.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.07.19 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I7/879

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