Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches

Abstract

Hybrid kinetic models represent a promising alternative to describe and evaluate the effect of multiple variables in the performance of complex chemical processes, since they combine system knowledge and extrapolability of the (semi-)mechanistic models in a wide range of reaction conditions with the adaptability and fast convergence of data-based approaches (e.g., artificial neural networks—ANNs). For the first time, a hybrid kinetic model for the direct DME synthesis was developed consisting of a reactor model, i.e., balance equations, and an ANN for the reaction kinetics. The accuracy, computational time, interpolation and extrapolation ability of the new hybrid model were compared to those of a lumped and a data-based model with the same validity range, using both simulations and experiments. The convergence of parameter estimation and simulations with the hybrid model is much faster than with the lumped model, and the predictions show a greater degree of accuracy within the models’ validity range. A satisfactory dimension and range extrapolation was reached when the extrapolated variable was included in the knowledge module of the model. This feature is particularly dependent on the network architecture and phenomena covered by the underlying model, and less on the experimental conditions evaluated during model development.