Physics-Informed Machine Learning for Modeling CO ₂ Capture from Scarce Data

Accurate modeling of complex industrial processes often relies on costly mechanistic simulations grounded in physical principles. In this paper, we investigate the subject of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> capture, a major environmental challenge, through the absorption column of an amine-based post-combustion process. The modeling of such unit at industrial scale faces two difficulties: (i) theoretical models, efficient at laboratory scale, might fail to fully reflect the complexity of the numerous intertwined phenomena occurring in the absorber, (ii) the cost and uncertainty of industrial observation data make purely data-driven approaches unfeasible. To tackle both this low data regime and inaccurate physical models, we envision this <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> capture problem through the lens of Physics-informed Machine Learning (PiML). We present a hybrid (data+knowledge) model where the scarce observation data complement the physical model, while the latter ensures that the predictions remain physically consistent. Beyond the standard use of simulation data for learning and the embedding of physical laws as regularization, the originality of our PiML algorithm compared to other methods in the literature lies in a physical prior assumption about the network architecture and its countercurrent flow learning process inspired by the column's operation. Our experimental results showcase a significant improvement in accuracy and highlight the potential of our augmented model for generalizing across domains, especially when data is scarce.