In this part, the approach for kinetic modelling of catalytic reactions using neural ODE models constrained with the information about kinetic and thermodynamic knowledge is described. As an example, the architecture of an ANN for kinetic modeling of CO2 hydrogenation into methane is described. Firstly, instead of using ANN for approximating molar flows of each component as in the baseline model, we suggest using ANN for approximating the reaction rates which are the feature of most interest to reaction engineers but also the basis for a detailed, mechanism-based kinetic modelling. In the case of the CO2 hydrogenation to methane, the considered reactions are RWGS and CO-FT. It is worth mentioning that reactions pathways can be obtained by analyzing selectivity-conversion dependencies for any reaction networks . So, the rates of formation/consumption of each compound can be calculated by using the stoichiometric matrix 𝑀 𝜈 of the corresponding reactions: