Application of neural network to estimation of catalyst deactivation in methanol conversion

The neural network was applied to the estimation of catalyst deactivation by taking, as an example, methanol conversion into hydrocarbons over ion-exchanged dealuminated mordenites. In the first series, it was attempted to estimate the deactivation rate constant, kd defined in −dA/dt = kdA where A is the degree of conversion, from the amount of strong acid sites and the catalyst composition such as the Si/Al ratio and the degree of ion exchange. The estimated rate constant agreed well in most cases with the experimentally obtained constant. The most serious exception was Ba ion-exchanged dealuminated mordenite which experimentally exhibited the slowest deactivation. Better agreement was obtained when the first-order reaction rate constant was used as A in the above equation instead of the degree of conversion. In the second series, it was shown that the neural network has a strong ability to extrapolate the catalyst decay curve even without knowing catalyst composition and properties, especially when the first-order reaction rate constant was used to represent the catalyst activity. All of these results clearly demonstrate that the neural network is a powerful tool to estimate the deactivation behaviour of catalysts.