The proposed regression modeling method enhances prediction accuracy in chemical processes, demonstrating significant advances with the soft sensor approach.
Key evidence shows that the method achieves a prediction accuracy increase of over 15% compared to traditional methods.
The approach utilizes variational autoencoders combined with vine copula for robust modeling of complex interdependencies among variables.
This method may enable improved process control in industrial applications, potentially enhancing efficiency and safety.