基于量子自适应鸟群算法的锅炉NOx排放特性研究

doi:10.3969/j.issn.1000-1158.2017.06.25

Abstract
Figure/Table
References (16)
Related Citation (15)

Download: PDF (1616 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract For the problem that the conventional generalized predictive method are not capable to accurately predict the NOx emission generated by boiler, a new approach with combining the advantages of quantum adaptation bird swarm algorithm (QBSA) and fast learning network (FLN) is put forward to get boiler NOx emission concentration model. Compared with bird swarm algorithm (BSA), differential evolution algorithm and particle swarm optimization, QBSA algorithm has presented its advantage on optimization accuracy and convergence speed. Finally, the accuracy of the prediction by the way of QBSA-FLN and BSA-FLN are tested by using the sample data under different working conditions. It turns out that QBSA-FLN model can offer better generalization capability and more accurate forecasting on predicting the mass concentration of NOx emission.

Key words： metrology NOx emission characteristics QBSA fast learning network

Received: 11 November 2015 Published: 27 September 2017

PACS:

TB99

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	NIU Pei-feng
	WANG Qiu-ya
	MA Yun-peng
	ZHAO Qing-chong
	CHEN Ke
	ZHAO Zhen

Cite this article:

NIU Pei-feng,WANG Qiu-ya,MA Yun-peng, et al. Study on NOx Emission from Boiler Based on Quantum Adaptation Bird Swarm Algorithm[J]. Acta Metrologica Sinica, 2017, 38(6): 770-775.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2017.06.25 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2017/V38/I6/770

［1］牛培峰,马云鹏,张京,等.基于相关向量机的电站锅炉 NOx燃烧优化［J］. 计量学报,2016,37(2):191-196.
［2］牛培峰,刘超,李国强,等.汽轮机热耗率多模型建模方法研究［J］. 计量学报, 2015, 36(3): 251-255.
［3］张维平, 赵文蕾, 李国强, 等. 基于粗糙集与最小二乘支持向量回归的汽轮机主蒸汽流量预测［J］. 计量学报, 2015, 36(1): 43-47.
［4］李国强. 新型人工智能技术研究及其在锅炉燃烧优化中的应用［D］. 秦皇岛:燕山大学, 2013.
［5］杨树欣, 李盼池. 基于量子蚁群优化的油田水淹层识别方法［J］. 计算机与数字工程, 2015,43( 2):173-177.
［6］Han K H, Kim J H. Quantum-inspired evolutionary algorithms with a new termination criterion, H & epsi; gate, and two-phase scheme［J］. Evolutionary Computation, IEEE Transactions on, 2004, 8(2): 156-169.
［7］Sun J, Xu W, Feng B. A global search strategy of quantum-behaved particle swarm optimization ［J］. IEEE Conference on Cybernetics and Intelligent Systerms, Singapore,2004, 1(11):111-116 .
［8］Sun J, Feng B, Xu W. Particle swarm optimization with particles having quantum behavior ［J］. congress on Evolutionary Computation, 2004, 1:325-331 .
［9］李盼池, 林晶晶. 基于Bloch球面搜索的混沌量子免疫算法［J］. 系统工程与电子技术, 2012, 34(12):2592-2598.
［10］黄泽霞, 俞攸红, 黄德才. 惯性权自适应调整的量子粒子群优化算法［J］. 上海交通大学学报, 2012, 46(2): 228-232.
［11］刘立峰, 孙赞东, 韩剑发,等. 量子粒子群模糊神经网络碳酸盐岩流体识别方法研究［J］. 地球物理学报, 2014,57(3):991-1000.
［12］Das S, Suganthan P N. Differential evolution: a survey of the state-of-the-art ［J］. Evolutionary Computation, IEEE Transactions on, 2011, 15(1): 4-31.
［13］Gandomi A H, Alavi A H. Krill herd: a new bio-inspired optimization algorithm ［J］. Communications in Nonlinear Science and Numerical Simulation, 2012, 17(12): 4831-4845.
［14］Yang X S, Deb S. Cuckoo search: recent advances and applications ［J］. Neural Computing & Applications, 2014, 24(1):169-174.
［15］Meng X B, Gao X Z, Lu L H, et al. A new bio-inspired optimisation algorithm: Bird Swarm Algorithm ［J］. Journal of Experimental & Theoretical Artificial Intelligence, 2016, 28(4): 673-687.
［16］曾建潮,崔志华,等.自然计算［M］.北京:国防工业出版社,2012:303-306.