By screening the auxiliary components and preparation methods

a Fe3O4 composite oxide catalyst was prepared for production of hydrogen from methane. Based on the properties of neural network

an improved back-propagation network model was developed to simulate the relationship between components of the catalyst and catalytic performances of the catalyst life and the formation rate of hydrogen. The model network structure and the computer-aided design procedures were established after investigation of the structural organization

the training method

the activation function

and the generalization ability of artificial neural network. Upon used the Levenberg-Marquardt training method

the network convergence was improved significantly and a formulation model of artificial neural network was achieved with strong generalization capability. To further enhance efficiency of catalyst design

a hybrid genetic algorithm was employed for global optimization of design. After six cycles of design optimization

a series formulation of Fe3O4 composite oxide catalysts for production of hydrogen from methane was developed. When one of the optimized catalysts was applied in hydrogen production

the catalyst life and formation rate of hydrogen were 4.46 h and 1.16 mmol·min-1·(g Fe)-1

respectively

which were better than those of previously reported catalysts.