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针对目前很多工业锅炉燃烧煤种发生变化时,控制效果下降,燃烧效率降低等问题,设计了一套在线煤种辨识系统,其核心采用模糊神经网络来构建辨识模型,综合了线性辨识方法和非线性神经网络建模方法的优势。能够利用过程历史数据辨识对象模型。利用试验仿真平台对该方法进行了仿真验证,结果显示了该方法辨识误差在2%以内,且具有适应范围广、计算方法简单等优点,该方法还可应用于锅炉其他重要热工参数的软测量,有很高的应用推广价值。
Aiming at the problems of many kinds of industrial boilers such as coal combustion, the control effect is decreased and the combustion efficiency is reduced, a set of on-line coal identification system is designed. The core of the system is fuzzy neural network to build the identification model, which combines the linear identification method and non- Advantages of linear neural network modeling methods. Process history data can be used to identify the object model. The experimental verification platform is used to verify the proposed method. The results show that the method has the advantages of wide range of acclimation, simple calculation and so on. The proposed method can also be applied to other important thermal parameters of boilers Measurement, there is a high value of the application of promotion.