A self-explanatory deep learning-based soft sensor induced by a physical diffusion process and its application in an industrial process

Key Points

• The self-explanatory soft sensor effectively captures diffusion processes in real-time, enhancing control.
• Evaluations show a significant reduction in error rates of predictions—achieving over 90% accuracy in various scenarios.
• Implementation involved a deep learning framework trained on diverse data sets to optimize the prediction model.
• This model may enable improved industrial process management, bridging gaps in physical modeling and operational efficiency.