recrutement

Thèse CIFRE : Modeling, Enablement and Validation of Prognostics and Health Management Solutions for Solid Oxide Electrolysis

Type de recrutement
Thèse
Durée
Urgent
oui
Rattachement
CEA LITEN / INSA Lyon Laboratoire Ampère / GENVIA
Fin de l'affichage

To efficiently produce high volumes of green H2, at economically compelling costs, the use of advanced technology components such as Solid Oxide Cells (SOC) and subsequent stacks, as core technology for large scale industrial systems, is an ambitious but credible path – supported by France and Europe given obvious decarbonation and industrialization benefits.

The first part of the PhD work will consist of understanding the operation of SOC technology and its use for industrial applications, gathering information about existing SO cells & stacks models and potential monitoring solutions. The PhD candidate will benefit from access to prior art, legacy and ongoing R&D work done by CEA teams/resources. She/he will also interact with the Genvia/CEA systems engineering/R&D teams in charge of developing High Temperature Electrolysis (HTE) modules for industrial demonstrators.

There are several aspects that go hand-in-hand for this research:

  1. The development of physics-based models of relevant components of the system, capable of providing relevant information about the state of the system and potential degradation phenomena, considering the model complexity (model reduction and simplification) and its use for diagnostic and optimization (control/optimization-oriented models)

  2. The design and choice of elements to serve as direct measurement, indirect measurement, and canary; these can be designed/chosen based on models and/or the understanding of the stack and module and/or experiments with prototypes

  3. The refinement of the models, developed in point 1, by exploiting available measurement information (data). The main objective of this point is to identify relevant parameters of the physics-based models, as well as potentially construct simple behavioral models for subsystems for which these models are not available or too complex.

  4. System parameters optimization against given H2 production rate scenarios: Tracking of internal states and parameters and apply prediction algorithms (to be developed and based to point 3) to estimate remaining time before maintenance.

One particular challenge is to explore and define, through collaboration work, design provisions for “passive elements” that could be added to the cells or stacks to assist with their location identification and performance monitoring, enable indirect measurements of key parameters such as temperature, voltage, currents or even pressure profiles with a resolution at the stack, ideally cell level. Such indirect measurements, assisted by design provisions, can be done permanently from outside the hot zone and/or through periodic inspection of the hot zone by metrology means under development. The provision for including passive elements into the design should also consider the possibility of adding elements with known degradation over time, i.e. so-called canaries, matching typical degradation of core elements which are present in high number and difficult to monitor individually.
In addition to theoretical/analytical work, we foresee opportunities for the PhD candidate to build prototypes and perform lab experiments as needed.

PhD Support Team

INSA Lyon, Laboratoire Ampère

  • Minh Tu PHAM (minh-tu.pham@insa-lyon.fr)

  • Federico BRIBIESCA ARGOMEDO (federico.bribiesca-argomedo@insa-lyon.fr)

CEA LITEN

  • Cyril BOURASSEAU (cyril.bourasseau@cea.fr)

  • Simon ALAMOME (simon.alamome@cea.fr)

  • Mathias GERARD (mathias.gerard@cea.fr)

GENVIA

  • Marian Faur (MFaur@slb.com)