煤气化过程的混合神经网络模拟  

Modeling approach to coal gasification using hybrid neural networks

作　　者：郭兵[1] 沈幼庭[1] 

机构地区：[1]清华大学热能工程系

出　　处：《清华大学学报（自然科学版）》1997年第2期11-15,共5页Journal of Tsinghua University(Science and Technology)

基　　金：国家自然科学基金

摘　　要：为研究循环流化床煤气—蒸汽联产炉的气化室过程，在一个以蒸汽为流化介质的批给料小型常压流化床装置上，进行了煤的气化实验。提出了一个煤气化过程的混合神经网络模型，包括一个基本原理模型，和由一组人工神经网络组成的模型参数估值器。以晋城无烟煤的实验数据对人工神经网络进行了训练，获得了较好的过程模拟结果。提出了一个描述焦的综合反应性的参数“活性焦比”，并用神经网络对晋城无烟煤的这一参数进行了识别，得到了该煤种的这一参数随温度变化的情况。Coal gasification was carried out in a bench scale fluidized bed gasifier. The gasifier operated at atmospheric pressure with steam as fluidizing medium. The background of this study was the gas steam cogeneration system. A hybrid neural network model was synthesized to predict the gas production rates. The model consists of a first principle partial model and a neural network parameter estimator. The model was trained with the experimental gasification data of Jincheng anthracite coal. The model had a good performance of process simulation. A parameter called “fraction of active char” was proposed in the model. Neural network performed identification of this parameter and obtained its variation with temperature, with respect to Jincheng anthracite.

关 键 词：煤气化 气化过程 过程模拟 神经网络 流化床 

分 类 号：TQ546.8[化学工程—煤化学工程]