Abstract
The identification of predictive mathematical model structures (i.e. set of model equations) is essential for the development of digital twin models of chemical reactor systems. Recent work demonstrated the use of artificial neural networks (ANNs) for kinetic model recognition in a conceptual batch reaction experimental system. In practical chemical processes, however, system behaviour is governed not only by reaction kinetics but also by reactor hydrodynamics and system thermodynamics. While a very recent study incorporated hydrodynamic effects, this work integrates the three aspects: reaction kinetics, reactor hydrodynamics, and system thermodynamics, to develop a general reactor modelling recognition framework. The framework, which comprises three modules: 1) model generator module; 2) data generation module; and 3) ANN classifier module, was applied to a case study of benzoic acid esterification in a Taylor vortex flow reactor system. Analysing the framework's sensitivity, results showed that ANN performance in classification deteriorates under increasing measurement noise but can be improved by increasing the number of simulated experiments. Further results show that extensive hyperparameter optimisation of ANN architectures provides no benefit over a fixed ANN architecture. This study highlights the potential of ANN-based frameworks for reactor model recognition while underscoring the dominant role of experimental design and data quality over network hyperparameter tuning.