화학공학소재연구정보센터
Computers & Chemical Engineering, Vol.25, No.9-10, 1241-1250, 2001
Modeling steady-state heterogeneous gas-solid reactors using feedforward neural networks
A new method for solving gas-solid heterogeneous reactors is proposed. Mass balance inside the pellet (numerical integration of a differential equations system) is replaced by an analytical function, which functionality corresponds to an adequate trained three-layer feedforward neural network. The global reaction rate evaluated by using this function includes the complex phenomena of simultaneous diffusion and chemical reaction into the solid. The methodology was successfully applied to the steam reforming of methane. Both methods are compared. Results of the reactor simulation are very similar in both cases but the one that used neural networks is about 20 times faster. The method proposed could also be applied to any type of two-phase heterogeneous reactors.