Modeling of batch reactor based on structure approaching hybrid neural networks approach

CAO Liulin; LI Xiaoguang; WANG Jing

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Modeling of batch reactor based on structure approaching hybrid neural networks approach

更新时间：2025-12-10

- Modeling of batch reactor based on structure approaching hybrid neural networks approach
- CIESC Journal Issue 4, Pages: 958-963(2008)
- 作者机构：
  
  北京化工大学信息科学与技术学院自动化研究所,北京,100029
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 April 2008，
  
  Published：05 April 2008
- 稿件说明：
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CAO Liulin, LI Xiaoguang, WANG Jing. Modeling of batch reactor based on structure approaching hybrid neural networks approach[J]. CIESC Journal, 2008, (4): 958-963.
DOI：

CAO Liulin, LI Xiaoguang, WANG Jing. Modeling of batch reactor based on structure approaching hybrid neural networks approach[J]. CIESC Journal, 2008, (4): 958-963. DOI：

摘要

提出了一种新的混合神经网络建模方法——结构逼近式混合神经网络。基于此结构建立的混合神经网络可以充分利用已知非线性系统的结构信息，使神经网络“灰盒”化，更好地解释和描述系统各变量间的因果关系，从而提高网络的建模精度和模型的可靠性。本文介绍了这类神经网络的基本特性、拓扑结构和训练方法。报告了一个典型放热液相二级平行间歇反应的建模过程;并针对间歇反应过程测量滞后的情况，与两种不同的混合神经网络模型作了比较，仿真和比较结果证明了方法的有效性。

Abstract

A new hybrid neural networks modeling approach

named structure approaching hybrid neural network (SAHNN) is presented in this paper.The characteristics and structure of this approach are introduced.The approach which combines first-principles model with neural networks fully utilizes structural information of known nonlinear system

makes neural networks a “grey-box”

describes and explains the consequence relation of system variables in a better way.A detailed analysis of SAHNN modeling was performed.Considering delayed measurement of outputs

simulation of a chemical batch reactor was performed and comparison of two types of hybrid neural network (HNN) was made.The results of simulation and comparison showed that the approach was a promising tool to model complicated nonlinear system effectively and could be utilized as a vehicle to control and optimize chemical reactors.

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