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.