Chaotic generalized differential evolution and its application in Texaco gasification process

XU Wei; GU Xingsheng; SUN Youxian

doi:10.3969/j.issn.0438-1157.2012.09.033

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Chaotic generalized differential evolution and its application in Texaco gasification process

更新时间：2025-12-10

- Chaotic generalized differential evolution and its application in Texaco gasification process
- CIESC Journal Issue 9, Pages: 2882-2886(2012)
- 作者机构：
  
  1. 浙江大学工业控制技术国家重点实验室,浙江,杭州,310027
  2. 华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海,200237
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2012.09.033
  CLC： P273
- Online First：05 September 2012，
  
  Published：05 September 2012
- 稿件说明：
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XU Wei, GU Xingsheng, SUN Youxian. Chaotic generalized differential evolution and its application in Texaco gasification process[J]. CIESC Journal, 2012, (9): 2882-2886.
DOI：

XU Wei, GU Xingsheng, SUN Youxian. Chaotic generalized differential evolution and its application in Texaco gasification process[J]. CIESC Journal, 2012, (9): 2882-2886. DOI： 10.3969/j.issn.0438-1157.2012.09.033.

摘要

在Texaco水煤浆气化工艺中

合成气中各组分的含量是衡量气化效率的关键参数。以某厂Texaco气化装置为研究背景

设计了一种合成气组分含量的预测模型。该模型选取三层前馈神经网络结构

并采用一种具有广义差分项的混沌差分进化算法(ChaoDEGD)作为模型参数的学习方法。ChaoDEGD算法在差分进化算法的变异操作中引入了广义的个体差异信息

并在不同进化时期

对不同适应度等级的个体施加混沌映射

保证了种群的多样性

帮助种群有效跳出了局部极小点。实验结果表明

基于ChaoDEGD的神经网络预测模型能够较好地估计合成气中CO、H2、CO2三类关键组分的含量

为Texaco水煤浆气化过程的安全稳定运行提供了有利指导。

Abstract

In the Texaco gasification process

the concentrations of syngas components are the key parameters to evaluate the gasification efficiency.A prediction model of the syngas components is designed for application in a real-world fertilizer plant.The model is a three-layer feedforward neural network

which adopts a chaotic differential evolution with generalized differentials(ChaoDEGD)as the learning algorithm.In ChaoDEGD

the generalized differential information between individuals is introduced into the mutation operation.Furthermore

chaotic mapping is brought on different individuals according to fitness ranking at each evolution phase

which preserves the population diversity so as to escape from the local minima.The experimental results indicate that ChaoDEGD is a competitive optimization algorithm and ChaoDEGD-NN based prediction model performs well in estimating the concentrations of CO

CO2 in Texaco syngas.This would provide valuable instructions for the safety and stability of the Texaco gasification.

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