The approach is based on constraining the architecture of neural ODE by integrating general knowledge about kinetics and thermodynamics of a catalytic process. A numerical experiment demonstrated that KCNODE model can be used for kinetic modeling of CO2 hydrogenation to methane using a small data set. The method does work even in presence of noise in the data.