Physics-informed neural networks for dynamic process operations with limited physical knowledge and data

Computers & Chemical Engineering, vol. 192, pp. 108899

Abstract

In chemical engineering , process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for modeling dynamic processes with incomplete mechanistic semi-explicit differential–algebraic equation systems and scarce process data. In particular, we focus on estimating states for which neither direct observational data nor constitutive equations are available. We propose an easy-to-apply heuristic to assess whether estimation of such states may be possible. As numerical examples, we consider a continuously stirred tank reactor and a liquid–liquid separator. We find that PINNs can infer immeasurable states with reasonable accuracy, even if respective constitutive equations are unknown. We thus show that PINNs are capable of modeling processes when relatively few experimental data and only partially known mechanistic descriptions are available, and conclude that they constitute a promising avenue that warrants further investigation.

Authors 6

1. Mehmet Velioglu Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute of Climate and Energy Systems, Energy Systems Engineering (ICE-1), Forschungszentrum Jülich GmbH, Jülich 52425, Germany

   RWTH Aachen University, Aachen 52062, Germany

2. Song Zhai Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Fluid Process Engineering (AVT.FVT), Aachen 52074, Germany

3. Sophia Rupprecht

   Forschungszentrum Jülich · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, 2629 HZ, Delft, The Netherlands

   Institute of Climate and Energy Systems, Energy Systems Engineering (ICE-1), Forschungszentrum Jülich GmbH, Jülich 52425, Germany

4. Alexander Mitsos Aachen

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Climate and Energy Systems, Energy Systems Engineering (ICE-1), Forschungszentrum Jülich GmbH, Jülich 52425, Germany

   JARA-ENERGY, Jülich 52425, Germany

   RWTH Aachen University, Process Systems Engineering (AVT.SVT), Aachen, 52074, Germany

5. Andreas Jupke Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Fluid Process Engineering (AVT.FVT), Aachen 52074, Germany

6. Manuel Dahmen corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Climate and Energy Systems, Energy Systems Engineering (ICE-1), Forschungszentrum Jülich GmbH, Jülich 52425, Germany