Partnership Inria - IFPEN
(2025-2028)

“This newly signed 2025-2028 project is part of the national strategic partnership between Inria and IFPEN in support of the energy transition, which led to the creation of a joint laboratory between the two partners in 2020. It deals with Physics-informed Machine Learning, especially with hybrid deep-learning models that combines physical laws with data-driven approaches. Their promise is to be more interpretable, more robust when facing outliers, and to lead to a more plausible physical behavior than their pure-machine-learning counterparts. The application case is post-combustion carbon capture, a process of major importance to tackle hard-to- abate greenhouse gas emissions of some key industrial sectors. The absorption column of this type of unit is modeled using a rigorous description of the physical mechanisms responsible for the transfer of acid gas to the liquid solvent. Nonetheless, this model fails to predict some experimental tendencies observed on industrial units, and these discrepancies cannot be explained by a specific phenomenon. In this context, the objective of this joint project (including a PhD thesis co-supervised by Rémi Emonet and Marc Sebban) is to design new hybrid PiML approaches, which incorporate laws from chemistry, to improve the precision of the absorption column description.”