基于混合鸡群算法和核极端学习机的锅炉NOx排放的预测

doi:10.3969/j.issn.1000-1158.2019.05.32

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Abstract Taking a 300 MW subcritical circulating fluidized bed boiler as an object of study, the NOx emission of the boiler was predicted accurately. A model about NOx emission from different working conditions was established using hybrid chicken swarm optimization based on simulated annealing(SACSO) and kernel extreme learning machine(KELM). By comparing in the differential evolution algorithm(DE), the particle swarm optimization(PSO) and the original chicken swarm optimization(CSO), the superiority of the improved algorithm was proved. Then, several models were compared in traditional BP algorithm, support vector machine (SVM), extreme learning machine (ELM) and KELM.The Finally determinded SACOS-KELM model has higher prediction accuracy, stability and better generalization ability, so this model is a good choice for boiler NOx emission in modeling and prediction.

Key words： metrology NOx emission circulating fluidized bed boiler simulated annealing algorithm chicken swarm algorithm SVM extreme learning machine with kernels

Received: 24 January 2018 Published: 01 September 2019

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TB99

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	Articles by authors
	NIU Pei-feng
	DING Xiang
	LIU Nan
	CHANG Ling-fang
	ZHANG Xian-chen

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NIU Pei-feng,DING Xiang,LIU Nan, et al. Prediction of Boiler NOx Emission Based on Mixed Chicken Swarm Algorithm and Kernel Extreme Learning Machine[J]. Acta Metrologica Sinica, 2019, 40(5): 929-936.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.05.32 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I5/929

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