NEURAL NETWORK MODELING OF THE PROPERTIES OF MULTICOMPONENT MIXTURES
A neural network model for the vapor phase fraction of a multicomponent mixture calculating is proposed. The analysis of the influence of the mixture state and the ratio of the components consumption on the desired value is carried out. Three classes of aggregates of the mixture parameters are identified, which make it possible to significantly reduce the computational costs and calculate the value of the vapor phase fraction only for one class. As a result, the structure of the neural network model is presented in the form of a cascade of a neuroclassifier and a regression neural network. A software package for constructing a training set has been developed. The selection of the architecture and training of each of the components of the neural network model was carried out, which ultimately ensured a high quality of the calculation of the vapor phase of a given mixture.