Optimization of Thermochemical Energy Storage Reactors Using Machine Learning

Kurzfassung

Thermochemical energy storage (TCES), where thermal energy is stored in a reversible chemical reaction in a porous powder bed, is a promising technology for large-scale and long-term thermal energy storage. It has been under long-standing investigation for prospective applications, such as the capture of excess heat from industrial processes or storing energy in concentrated solar power plants, to offset their unpredictable energy generation. This study investigates TCES in the SrBr2-system, which offers a high energy capacity and near-perfect reversibility. However, the scaling up of these reactors is hindered by the limited heat transfer from the heat source, such as reactor walls, to the powder bed. To address this challenge, heat conducting structures, such as fins, are incorporated into the bed to enhance thermal contact and shorten transport paths. Moreover, structural changes through mechanical and physical alteration of the powder bed, as well as changes in the microstructure, lead to changing heat and mass transport properties of the porous medium during cycling. In this study, we are using ML-techniques to improve the heat transfer inside the reactor by designing optimized heat conducting structures. Even though modeling these effects can be done in principle, by simulating the heat and mass transport inside the reactor, direct simulations are prohibitively time consuming. Hence, we construct an ML-Based surrogate model, which is trained with a physical simulation and which can predict the performance of the reactor based on the structures geometry (e.g. MSNet or its autoregressive form AR-MSNet). This can be done either by training a neural network on simulated data or by directly incorporating the physical model equations into the loss function of the network training algorithm. The surrogate model is then coupled with a topology optimization algorithm based on the level-set method, which is used to calculate optimal geometries for the heat conducting structures. Our contribution will center on the surrogate modelling techniques employed and the optimization results obtained.