Can artificial neural networks help kinetic modeling? Case study: methanol reactor

this work an Artificial Neural Networks (ANN) pproach predicts the deactivation of the catalyst at different operating conditions as a function of time. By the aid of ANN sufficient data were generated which vary with time and agree very well with experimental data. Capability of model in generating deactivation data in different temperatures, pressures and feed compositions was excellent. The proposed method has great potential as a means to compensate for lack of the phenomenological kinetic modeling techniques.

Keywords:

Catalyst Deactivation , Methanol Synthesis , Neural Network