On-line prediction of a fixed-bed reactor using K-L expansion and neural networks

摘要

An on-line prediction scheme combining the Karhunnen-Loeve expansion and a recur- rent neural network for a wall-cooled fixed-bed reactor is presented. Benzene oxidation in a pilot- scale, single tube fixed-bed reactor is chosen as a working system and a pseudo-homogeneous two- dimensional model is used to generate simulation data to investigate the prediction scheme presented under randomly changing operating conditions. The scheme consisting of the K-L expansion and neural network performs satisfactorily for on-line prediction of reaction yield and bed temperatures.