混沌广义差分进化算法及其在Texaco气化过程中的应用

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。

Chaotic generalized differential evolution and its application in Texaco gasification process

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,H2,CO2 in Texaco syngas.This would provide valuable instructions for the safety and stability of the Texaco gasification.