大型四角切圆燃烧锅炉NO_x排放特性的神经网络模型  

AN ARTIFICIAL NEURAL NETWORK MODEL ON NO_x EMISSION PROPERTY OF A HIGH CAPACITY TANGENTIALLY FIRING BOILER

作　　者：周昊[1] 朱洪波[2] 茅建波[1] 廖宏楷[2] 岑可法[1] 

机构地区：[1]能源清洁利用和环境工程教育部重点实验室浙江大学热能工程研究所,浙江杭州310027 [2]广东省电力集团公司,广东广州510600

出　　处：《中国电机工程学报》2002年第1期33-37,共5页Proceedings of the CSEE

基　　金：国家重点基础研究发展规划基金资助项目 ( 973)(G19990 2 2 2 0 4)~~

摘　　要：随着环保要求的不断提高 ,大型燃煤电厂锅炉的NOx排放特性日益受到关注 ,但其排放特性复杂 ,受煤种、锅炉基金项目 :国家重点基础研究发展规划基金资助项目 ( 973)(G19990 2 2 2 0 4)。SubsidizedbytheSpecialFundsformajorStateBasicResearchPro jectsofChina( 973) (G19990 2 2 2 0 4) .设计结构、操作参数等多种因素影响。在对某台 6 0 0MW四角切圆燃煤电厂锅炉的NOx 排放特性进行多工况热态测试的基础上 ,应用人工神经网络的非线性动力学特性及自学习特性 ,建立了大型四角切圆燃烧锅炉NOx 排放特性的神经网络模型 ,并对此模型进行了校验。结果表明 ,该模型能根据燃煤特性及各种操作参数准确预报锅炉在不同工况下的NOx 排放特性 ,如结合全局寻优技术 ,可为大型电厂锅炉通过燃烧调整降低NOx 排放提供有效手段。With the developing restrict environmental protection demand, more attention is paid to the NO x emission property of the high capacity tangential firing boiler , but the NO x emission property of coal burned boiler is complicated, it is affected by many factors, such as coal character, boiler's load, air distribution, boiler style, burner style, furnace temperature, excess air ratio, pulverized coal fineness and the uniformity of the air and coal distribution, etc. In this paper, the NO x emission property of a 600MW utility tangentially firing coal burned boiler is experimental investigated, and taking advantage of the nonlinear dynamics characteristics and self-learning characteristics of artificial neural network, an artificial neural network model on NO x emission property of the high capacity boiler is developed and verified. The results illustrates that such a model can predicate the NO x emission under various operating conditions, if combined with the optimization algorithm, the operator can find the best operation condition to decrease the NO x emission.

关 键 词：大型燃煤电厂 锅炉 四角切圆燃烧 氮氧化物 排放特性 神经网络 

分 类 号：TM621.2[电气工程—电力系统及自动化] X773[环境科学与工程—环境工程]