Virtual Reactor and Dynamic Reaction Analysis by Using Neural Networks

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In the present study, artificial neural networks have been applied to modeling the reaction kinetics of carbon-NOx reaction as a "virtual reaction system". The unsteady-state kinetic experiments were carried out using a fixed-bed flow reactor at various different experimental conditions altered continuously. The changes in the concentrations of O2, CO2, N2, NO and NO2 were measured as time series data of reaction kinetics by gas chromatography and a NOx meter. The time series data of reaction kinetics (the amount of catalyst and carbon, temperature and concentrations of reaction species versus time) was used for learning the neural networks. The mechanisms of carbon-NOx reaction are discussed based on the virtual experiments by means of trained neural network model.