As described above, there are many parameters to be determined when identifying a MKM. This process of parameter identification is of critical importance for the success of the endeavor. Unfortunately, conventional parameter fitting methods often struggle in multi-parameter settings making brute force identification of kinetic parameters unfeasible . The use of first-principle data obtained by computational chemistry like density functional theory (DFT) might eliminate the need for parameter fitting routines but comes at extremely high computational costs making it equally unfeasible to apply to complex multi-component systems . Thus, many of the currently employed approaches attempt to reduce the parameters (and thereby the required computational time) as far as possible. The different ways this is achieved separates the authors: While some start from the most complex possible mechanism and systematically reduce complexity by employing knowledge about discovered RDS on the fly , others take the inverse approach and start at simple models. After obtaining reasonable agreement with experimental data, further complexity is added to describe deviations only where needed . Others again circumvent expensive DFT-calculation and use shortcut methods estimating kinetic parameters only to dynamically refine them where needed . All three approaches are viable but still require some amount of parameter fitting to scale results from DFT or approximate them. Though progress towards reliable global minimization algorithms is being made, at the time of writing this article no catch-all solution exists. Chacko et al. recently revisited already published work and were able to enhance prediction accuracy by fitting kinetic parameters through basin hopping, a minimization algorithm based on local exploitation with simultaneous global exploration steps . This success once again underlines the importance of accurate parameter identification when attempting to combine first-principle with experimental data by MKMs. From an industrial modeling standpoint however, the identification of physically accurate parameters might not even be necessary. For process design a reasonably accurate rate prediction is oftentimes enough. This realization and the advent of cheap computational power lead to the development of surrogate machinelearning driven models reproducing kinetic behavior over a wide range of conditions. These range from black-box-style deep neural networks over physically-informed semi-interpretable kinetic expressions into fully interpretable digital twins of physical and chemical processes. Especially the latter are of great interest since they allow for the accurate prediction of reaction rates with the added benefit of gaining insight into fundamental kinetic properties albeit from a phenomenological perspective. The chemical reaction neural network (CRNN) formalism as an example for such a digital twin will be the foundation of this work.