Neural networks for modelling of kinetic reaction data applicable to catalyst scale up and process control and optimisation in the frame of combinatorial catalysis

This work describes an application of artificial neural networks (ANNs) for modelling the kinetics of catalytic reactions using methods not based on fundamental knowledge. Thus, neural networks have been used to model the behaviour of different reactions under different reactor conditions. The modelling of catalytic reactions by neural networks has been demonstrated, and the influence of experimental error in input data has been estimated. In addition, a novel methodology for modelling catalytic data employing already-trained neural networks has been systematically studied by using experimental results from the catalytic hydroisomerization of different n-paraffins. It can be then expected that a new reaction system can be rapidly analysed with a small number of experiments if a library of well-trained neural networks which represent a series of different reaction networks is constructed.