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化工学报  2012 

混沌广义差分进化算法及其在Texaco气化过程中的应用

DOI: 10.3969/j.issn.0438-1157.2012.09.033, PP. 2882-2886

Keywords: 差分进化算法,混沌映射,Texaco合成气

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Abstract:

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。

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