基于人工智能技术的电站锅炉最优氧量预测  

FORECAST OF OPTIMAL OXYGEN CONTENT IN FLUE GAS FROM UTILITY BOILER BASED ON ARTIFICIAL INTELLIGENT TECHNOLOGY

作　　者：赵绪新[1] 

机构地区：[1]深圳大学,广东深圳518060

出　　处：《热力发电》2006年第10期43-45,共3页Thermal Power Generation

基　　金：深圳市科技计划资助项目(200436)

摘　　要：用人工神经网络建立了炉膛出口烟气含氧量特性模型,并采用遗传算法进行寻优。针对某电厂200 MW燃煤机组锅炉的实际运行情况,进行最优氧量预测,预测结果和基于燃烧机理的定量分析结果一致,相对误差仅为0.033%,表明人工智能技术可有效地预测不同工况下电站锅炉的最优氧量。A characteristic model of oxygen content in outlet flue gas of boiler has been established by using artificial neural network,and the optimal value being searched by adopting the genetic algorithm.Directing against the practical operation situation of coal-fired boiler for 200 MW units in one power plant,forecast of optimal oxygen content has been carried out,the result of forecast is identical with that of quantitative analysis based on the combustion mechanisms,the relative error only to be 0.033%.It is shown that the artificial intelligent technology can effectively be used to forecast the optimal oxygen content in outlet flue gas from utility boiler under different operating conditions.

关 键 词：锅炉 运行优化 烟气 含氧量 人工智能 

分 类 号：TK227.1[动力工程及工程热物理—动力机械及工程]