基于RBF神经网络的电厂入炉煤元素分析  

Ultimate Analysis Technique Using Radial Basis Function Neural Network

作　　者：陈耀森[1] 陈荣生[2] 杨亚平[1] 

机构地区：[1]东南大学热能工程研究所,江苏南京210096 [2]福建电力职业技术学院,福建泉州362000

出　　处：《江苏电机工程》2006年第6期4-5,共2页Jiangsu Electrical Engineering

摘　　要：通过分析电厂锅炉燃煤的燃烧产物,利用产物与燃料之间的质量平衡以及对燃烧条件的监测,建立了基于径向基函数(RBF)神经网络的电厂入炉煤元素分析软测量模型。根据应用实例,实际仿真表明该模型计算速度快,具有一定的准确度,符合电厂在线监测煤质的要求,相对于传统煤元素分析和在线分析仪,优点显著。The paper establishes soft measurement models of Ultimate Analysis of Coal into Furnace in Power Plant Based on Radial Basis Function Neural Network, through analysis of furnace coal combustion product, using the quality equilibrium of fuel and the monitoring of firing conditions. According to the simulation of some practical examples, the calculation speed and precision is good, which meets the necessity of on-line monitoring of coal in power plants. Comparing traditional coal ultimate analysis technique and on-line analyzers, its merits are outstanding.

关 键 词：电厂 锅炉 燃煤 元素分析 软测量 RBF神经网络 烟气分析 

分 类 号：TQ533.1[化学工程—煤化学工程]