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基于人工神经网络和模拟退火算法的电站锅炉低氮燃烧优化

[The utility boiler low NOx combustion optimization based on ANN and simulated annealing algorithm].

作者信息

Zhou Hao, Qian Xinping, Zheng Ligang, Weng Anxin, Cen Kefa

机构信息

Clean Energy and Environment Engineering Key Lab of MOE, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China.

出版信息

Huan Jing Ke Xue. 2003 Nov;24(6):63-7.

Abstract

With the developing restrict environmental protection demand, more attention was paid on the low NOx combustion optimizing technology for its cheap and easy property. In this work, field experiments on the NOx emissions characteristics of a 600 MW coal-fired boiler were carried out, on the base of the artificial neural network (ANN) modeling, the simulated annealing (SA) algorithm was employed to optimize the boiler combustion to achieve a low NOx emissions concentration, and the combustion scheme was obtained. Two sets of SA parameters were adopted to find a better SA scheme, the result show that the parameters of T0 = 50 K, alpha = 0.6 can lead to a better optimizing process. This work can give the foundation of the boiler low NOx combustion on-line control technology.

摘要

随着环保要求的不断提高,低成本且易于实施的低氮氧化物燃烧优化技术受到了更多关注。在这项工作中,对一台600兆瓦燃煤锅炉的氮氧化物排放特性进行了现场试验,基于人工神经网络(ANN)建模,采用模拟退火(SA)算法对锅炉燃烧进行优化,以实现低氮氧化物排放浓度,并获得了燃烧方案。采用两组SA参数来寻找更好的SA方案,结果表明,T0 = 50 K、alpha = 0.6的参数可导致更好的优化过程。这项工作可为锅炉低氮氧化物燃烧在线控制技术奠定基础。

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