Physics-Informed Neural Networks for Process Systems: Handling Plant-Model Mismatch

This work addresses the problem of leveraging first-principles knowledge with data-driven techniques in the Physics-Informed/Inspired Neural Network (PINN) framework to handle plant–model mismatch. To this end, a PINN is developed utilizing the first-principles model of the system and plant data and demonstrated to handle plant–model mismatch. The PINN is compared with another dynamic modeling technique, a Recurrent Neural Network (RNN), and …