Neural ODE is an attractive approach for data-driven modelling of dynamic systems including kinetic models . In fact, ANN can be used for approximating reaction rate expressions that are usually unknown. Weiqi Ji and Sili Deng suggested a new type of neural ODEchemical reaction neural network (CRNN) for autonomous identifying reaction pathways. The architecture of CRNN is based on the law of mass action and the Arrhenius law. Despite of successfully demonstration for numerical examples , in the case of complex dynamic system of biomass pyrolysis , it is difficult to interpret the developed CRNN model. This is caused by the fact that many chemical processes including heterogeneous and catalytic reactions cannot be described by simple power-law-like equations.