基于蚁群神经网络的飞灰含碳量测量方法  

Prediction Method of Unburned Carbon Content in Fly Ash Based on Ant Colony Algorithm

作　　者：张正友 钱家俊[2] 冯旭刚[2] 

机构地区：[1]安徽省马鞍山市质监局,安徽马鞍山243000 [2]安徽工业大学,安徽马鞍山243000

出　　处：《计测技术》2017年第1期18-20,共3页Metrology & Measurement Technology

摘　　要：飞灰含碳量是衡量电站锅炉燃烧效率的重要参数。飞灰含碳量的准确测量有助于调整锅炉燃烧,提高锅炉运行的经济性和安全性。本文采用了蚁群神经网络算法,利用蚁群算法对神经网络进行优化,将优化过后的神经网络用于飞灰含碳量的预测,并分析了经过蚁群神经算法与遗传神经网络的预测效果。Unburned carbon content of fly ash is an important index reflecting the combustion efficiency of utility boilers. Measuring the carbon content in the fly ash accurately is beneficial to the detection and adjustment of boiler combustion. This paper uses the ant colony neural network which optimized the initializing weights,thresholds and numbers of nodes in the hidden layer of the BP neural network. The optimized neural network is used to predict the unburned carbon content of fly ash. And the prediction result is also analyzed in this paper.

关 键 词：蚁群算法 神经网络 飞灰含碳量 

分 类 号：TB94[一般工业技术—计量学] TN081[机械工程—测试计量技术及仪器]