The Thermal and Solar Technologies Laboratory (L2TS) and the Energy Systems for Territories Laboratory (LSET), located at the CEA LITEN site in Le Bourget-de-Lac, are offering a cross-disciplinary PhD thesis combining thermodynamics and optimization using Artificial Intelligence.

Specifically, this doctoral research project involves developing a machine learning algorithm to optimize the control of absorption machines. These machines are thermodynamic cycles able to produce heat or cold from an intermediate heat input; thus, offering potential valorization of industrial waste heat or renewable energies, such as solar thermal. Heat exchange is made possible by the absorption and desorption reactions of a gaseous refrigerant in a fluid. Specifically, the NH3-H2O mixture will be used. The dynamic operation of these cycles is extremely complex because the operational variables, physical parameters, and hydrodynamic aspects are highly intertwined. Thus, the use of a neural network is particularly relevant for establishing an adaptive control strategy for these machines.

The thesis will have a theoretical aspect, involving the study and selection of the most suitable algorithm to address the problem, and an experimental aspect of validation on a prototype absorption machine. The project will also involve the design of a controller for implementation.