On-Line Prediction of a Fixed-Bed Reactor Using K-L Expansion and Neural Networks

摘要

An on-line prediction scheme combining the Karhunen-Love expansion and a recurrent neural network for a wall-cooled fixed-bed reactor is presented.Benzene oxidation in a pilotscale,single tube fixed-bed reactor is chosen as a working system and a pseudo-homogeneous twodimensional model is used to generate simulation data to investigate the prediction scheme presentedunder randomly changing operating conditions.The scheme consisting of the K-L expansion andneural network performs satisfactorily for on-line prediction of reaction yield and bed temperatures.