The developed approach could also be successfully applied for modeling the kinetics of CO2 hydrogenation to hydrocarbons based on experimental data. The obtained KCNODE model was able to describe reagent consumption and product formation as well as their distribution correctly. Because the KCNODE models can be trained on small data sets and are resistant to overfitting, the approach can be recommended for the fast development of kinetic models of any catalytic reaction; the KCNODE model can also help in developing kinetic models based on fundamental principles and for elucidating reaction mechanism. Training ANN models on small data does not incur high computational costs which makes the development such ANN approachable for many researchers. The KCNODE significantly expands the scope of usage of neural ODE for describing, analyzing, and developing models based on ordinary differential equations.