where 𝐹 𝑖 are molar flows of i-compound; 𝑃 Σ is the total pressure. It is worth mentioning that there are parameters like 𝑇 or 𝑃 Σ which are independent of 𝑉 and should be constant along the reactor length. Parameterized neural ODE (PNODE) suggested by Kookjin Lee and Eric J.Parish is an extension of neural ODE and was developed for cases when such independent parameters have to be taken into account. The main idea is to use additional encoder-decoder neural layers. Despite of authors demonstrated the effectiveness of PNODEs on benchmark problems from computational physics, the suggested approach still has a disadvantage. It is related to the complication of the architecture of neural ODEs and an increase in number of learnable parameters that require more data for model training. In our work we suggest a simpler approach for the problem of independent parameters that does not require to use additional ANN learnable variables. Our approach is to return zeros values as outputs of ANN for such parameters. This makes them to be constant and independent of 𝑉.