文献类型：期刊文献

中文题名：神经网络预测煤焦高温气化反应速率研究

英文题名：STUDY ON BACK-PROPAGATION NEURAL NETWORK MODELING OF PREDICTING THE GASIFICATION RATE UNDER THE ELEVATED TEMPERATURES

作者：张晓[1];吴诗勇[1];顾菁[1];吴幼青[1];高晋生[1]

机构：[1]华东理工大学能源化工系,上海硕士生200237

年份：2007

卷号：30

期号：2

起止页码：22

中文期刊名：煤炭转化

外文期刊名：Coal Conversion

收录：CSTPCD;;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：国家重点基础研究规划项目(2004CB217704)

语种：中文

中文关键词：煤气化;神经网络;L—M算法;高温

外文关键词：coal gasification,neuron network, L-M algorithm,elevated temperature

摘要：神华大柳塔煤和兖州北宿煤都是气流床气化的优良煤种.通过三种算法的比较,采用BP神经网络的L-M算法,分析煤的制焦终温与制焦升温速率、气化反应温度、Vdaf和煤焦H/C原子比等不同因子对煤焦气化反应速率模型预测精度的影响,建立了基于Matlab下神华大柳塔单煤种四因子和神华-兖州双煤种五因子煤焦高温气化反应速率神经网络预测模型,得到比较满意的结果,其相对误差分别是0.167和0.264.
In this paper, two typical coals for entrained flow gasifiers,Shenhua coal and Yanzhou coal were used for raw materials. The L-M algorithm was employed in the BP neuron network through the comparison of three different kinds of algorithms, and the effects of factors, such as the content of volatile matter（Valor） in raw coals, the ratio of H and C （H/C） in coal chars, pyrolysis temperature, heating rate and gasification temperature, on the prediction error of BP neuron network were investigated. The neuron network with four factors were applicable for predicting the gasification rates of a single coal （Shenhua coal）, while the neuron network with five factors for predicting those of different coals （Shenhua coal and Yanzhou coal）. The result showed that the corresponding relative errors, which were respectively 0. 167 and 0. 264, were quite little.