| 基于改进BP神经网络的煤催化气化预测模型研究 |
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| 引用本文： | 崔阳,徐龙,刘艳,马晓迅,杨建丽. 基于改进BP神经网络的煤催化气化预测模型研究[J]. 燃料化学学报, 2011, 39(2): 90-93 |
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| 作者姓名： | 崔阳 徐龙 刘艳 马晓迅 杨建丽 |
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| 作者单位： | 1.College of Chemical Engineering, Northwest University, Xian 710069, China,;2.State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China |
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| 基金项目： | 国家自然科学基金，教育部科学技术研究重点项目，煤转化国家重点实验室开放基金 |
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| 摘 要： | 采用改进的三层BP神经网络建立了煤催化气化反应失重率、气化初始温度和最大气化速率所对应温度的预测模型。结果表明，采用改进BP神经网络模型在此研究中可达到较高的精度，其最大预测误差分别为5.18% 、5.65% 、2.33%，明显小于归回公式的预测误差。
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| 关 键 词： | font-size: 9pt,line-height: 150%" >煤催化气化,进BP神经网络,归公式, |
| 收稿时间： | 2010-04-03 |
Prediction model of coal catalytic gasification based on the improved BP neural network |
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| CUI Yang,XU Long,LIU Yan,MA Xiao-xun,YANG Jian-li. Prediction model of coal catalytic gasification based on the improved BP neural network[J]. Journal of Fuel Chemistry and Technology, 2011, 39(2): 90-93 |
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| Authors: | CUI Yang XU Long LIU Yan MA Xiao-xun YANG Jian-li |
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| Abstract: | Coal catalytic gasification is a very complex process, which was affected by a lot of factors. A prediction model would be helpful for the understanding, design and optimization of such processing. Therefore, a prediction model for the weight loss, the initial temperature of gasification and the temperature of maximum gasification rate during coal catalytic gasification was established by using a three-layer improved Back Propagation (BP) neural network. The model prediction results indicate that the improved BP neural network has a high accuracy. Moreover, the maximum errors between the experimental and the predicted values are 5.18%, 5.65% and 2.33%, respectively, which are much smaller than those predicted by regression equation. |
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| Keywords: | font-size: 10.5pt,line-height: 150%" >coal catalytic gasification,improved BP neural network,regression equation, |
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