In recent years, following the first detection of Gravitational Waves (GWs), we have witnessed the birth of GW Astronomy. So far, there have been more than 50 events recorded, providing us with invaluable information about the nature of the merging binaries. An exceptional case is the event GW170817, a Neutron Star merger, which was observed with both gravitational and electromagnetic (EM) waves. From a single event alone, by combining both ways of observation, we were able to vastly improve our understanding of such cataclysmic events. In the near future, in particular, in the early 2030s, the ESA Laser Interferometer Space Antenna (LISA) is going to be launched. LISA is a space-borne Gravitational-Wave observatory that, in contrast to the present ground-based detectors, is going to be signal-dominated. The LISA data will give us the unique opportunity to observe the merger of supermassive black hole binary systems, which in combination with the EM observations will enable us to push our knowledge boundaries in astronomy, astrophysics, and cosmology. With EMILIA, we aspire to enable multi-messenger astronomy with LISA, by developing a low-latency data analysis pipeline based on Machine Learning techniques. Our proposed methodology will take into account the source confusion problem of LISA, where monochromatic signals and noise artefacts are going to be classified as such and subtracted from the data. We will then apply a fast semi-analytical algorithm on the residual data, in order to swiftly estimate the sky position and time of coalescence of chirping signals. Such a scheme will enable the synergy of LISA and optical observatories on Earth and in space. A prime example is that of the LISA-Athena missions synergy, which would probe the existence of electromagnetic counterpart of massive black hole mergers and extreme mass ratio inspirals, or phenomena like X-ray flares, disk re-brightening, and relativistic jet formations.

As per an EU estimate, the industrial sector accounts for 27% of the overall energy consumption and for the generation of 30% heat-related CO2 emissions. Industrial thermal processes account for 70 % of the energy demand, which translates to 20.8% of the entire EU energy demand. Waste heat recovery (WHR) is, thus, one of the next frontiers for energy-intensive industries. Thermal energy storage (TES) is a promising alternative to currently available WHR technologies, particularly for medium-high temperature settings. Latent heat storage, centred on the ability of a material, commonly referred to as the phase change material (PCM), to absorb/release heat isothermally during its transition from one state to another, faces several performance issues inherent to the material’s properties. Encapsulating PCMs in solid matrices, consisting of refractory materials, has been found to resolve most of these issues. These new materials can be used to store both sensible and latent heat (hybrid TES) potentially outperforming current TES systems. The properties of red mud (RM), a currently disregarded and potentially hazardous waste of the aluminium industry, make it an ideal candidate for PCM encapsulation. REDTHERM aims to scale up the recently discovered, by the researcher, red mud-molten salt material and demonstrate, for the first time, its performance in a novel medium-high temperature WHR layout using real industrial settings. In this way it can promote a novel and tangible business case of industrial symbiosis (circular economy) in which the waste product of the aluminium industry (RM) is valorised as a key component for medium-high temperature WHR systems to increase energy efficiency and decrease the carbon footprint of foundation industries with relevant interest (steel, cement, casting etc. This timely project can substantially contribute towards the EU’s 2050 sustainability agenda, while in parallel expanding the science of the promising field of hybrid-TES.