Neural network approach to support modelling of chemical reactors: problems, resolutions, criteria of application

New aspects of neural modelling of chemical reactors have been investigated in this study. An universal method to create a family of neural models, useful for the reactor and reacting system of any type, has been elaborated and presented. Based on this method a detailed analysis of the neural models has been performed. The proposed methods of modelling as well as a comparative analysis of the obtained results have been illustrated with the data obtained for a complex, catalytic hydrogenation of 2,4-dinitrotoluene performed at non-steady state conditions in a multiphase stirred tank reactor. The methods of choosing the input–output signals, the net architecture, the learning method, the number and quality of learning data have been proposed and their influence on the accuracy of obtained predictions have extensively been discussed. A comparison of two types of neural models: a global neural model and a hybrid neural model to a conventional reactor modelling has been performed. General conclusions and useful criteria have been formulated.