The neural network consists of 3 layers with 8, 8, and 5 nodes per layer, and with activation functions: hyperbolic tangent, exponential and linear, respectively. The first layer consists of 6 inputs: partial pressure of compounds (for CO2, CO, H2, H2O, CH4) and temperature. The output of the linear layer is expanded by concatenating with 3D zeros vector (for temperature, total pressure and N2 molar flow). Before training, the value of total pressure and temperature were scaled by dividing by the maximum values.