【作者】 崔震华；

【导师】 樊泉桂；

【作者基本信息】 华北电力大学（河北） ， 热能工程， 2007， 硕士

【摘要】 煤灰的结渣特性主要取决于煤灰中各化学成分的百分比含量,同时也取决于各物质的熔点温度。而煤灰中各化学成分含量与煤灰熔点也有一定联系,进而可以估计煤灰熔融特性。本文应用BP神经网络中的不同算法,来研究煤灰各组分含量与煤灰熔融特性之间的关系。通过对170组数据,应用神经网络进行分析,预测煤灰熔点温度,以初步分析BP神经网络各种算法的特点,以及在研究本文内容时的适用性,并通过改变主要参数进行试验,以提高BP神经网络预测煤灰熔融特性的精度。通过分析预测误差离散点,分析研究影响煤灰熔融特性的主要因素及相关程度。更多还原

【Abstract】 Ash slagging characteristics not only depends mainly on the percentage of ash content in the chemical composition,but also depends on the melting point temperature of the material.But in the ash various chemical composition content and the ash melting point also have certain relation,then may estimate the ash fusing characteristic.This article applies BP neural network to researching the relations betweens ash various components content and ash melting characteristic.Through to 170 groups of data,applying neural network to the analysis and forecasting the ash melting temperature,these promulgates each algorithm of BP neural network’s characteristic as well as the advantages in researching of this article’s content.Carrying on the experiment when changing the main parameter,to increaseing the precision of the BP neural network’s forecasting characteristic.On the end,the article through analysis forecast error spot,promulgating the primary factor of influencing melting characteristic.更多还原