文献类型：期刊文献

中文题名：基于BP神经网络预测气流床气化炉炉膛温度的试验研究

英文题名：The experimental study of wall temperature in entrained-flow gasifier based on BP neural network

作者：郭庆华[1];梁钦锋[1];颜卓勇[1];于广锁[1];于遵宏[1]

机构：[1]华东理工大学洁净煤技术研究所,上海200237

年份：2007

卷号：24

期号：5

起止页码：617

中文期刊名：计算机与应用化学

外文期刊名：Computers and Applied Chemistry

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

基金：国家重点基础研究发展计划(2004CB217703);上海市启明星计划(03QF14013)

语种：中文

中文关键词：BP神经网络;气流床气化炉;炉膛温度;Matlab

外文关键词：BP neural network, entrained flow gasifier, wall temperature, Matlab

摘要：气流床煤气化技术,是当今煤炭清洁利用的龙头技术。控制气化炉操作温度是保证气化炉长期稳定运行的关键。本文利用三层BP神经网络,结合Matlab神经网络工具箱,分析和预测四喷嘴对置式气流床气化炉试验装置其炉膛的最高温度。将预测的结果和实验数据作比较,结果表明,该网络模型的预测值与气化炉不同负荷下最高温度试验值几乎吻合,是一种可行的数据处理方法。
The entrained-flow gasification technology is the first-line technology of coal utilization. Controlling of the operation temperature in gasifier is the key to ensure the long period steady operation. In this paper, the three layers BP neural network and Matlab neural network toolbox are used to analyze the data of the highest wall temperature in the entralned-flow gasifier with opposed multi-burner. Comparison of the predicted results and the experimental data proved that the predicted values of this model can fit the highest real temperature well at various conditions, and this method of data processing is feasible.