SisAl Pilot aims to demonstrate a patented novel industrial process to produce silicon (Si, a critical raw material), enabling a shift from today’s carbothermic Submerged Arc Furnace (SAF) process to a far more environmentally and economically alternative: an aluminothermic reduction of quartz in slag that utilizes secondary raw materials such as aluminium (Al) scrap and dross, as replacements for carbon reductants used today. SisAl Pilot represents a path-breaking approach, and a strong contribution to “circularity” through industrial symbiosis where the Al industry will act as both a raw material supplier and end user to the Si industry. Across sectors, SisAl Pilot will give substantial reductions in material yield losses, enhanced valorisation of waste- and by-product streams, at a 3 X lower energy consumption and radically lower emissions of CO2 and harmful pollutants, at a considerably lower cost. The SisAl Pilot project brings together raw material provider (Erimsa), silicon and aluminium key actors (Wacker, Elkem, DOW, Silicor, SiQAl, Hydro, FRey, Befesa, MYTIL), SME´s/consultants/ equipment manufacturers (BNW, SIMTEC, WS and SBC) and research organisations (NTNU, RWTH, NTUA, ITMATI, SINTEF, HZDR, MINTEK) to demonstrate the SisAl process with different raw materials and product outputs in 4 different countries. These pilots will be accompanied by environmental, economic and technological benchmarking, and industrial business cases will be assessed for locations in Norway, Iceland, Germany, Spain and Greece. The timing of SisAl Pilot is impeccable; the transformation to a circular economy, the strongly enhanced focus on climate and future expected EU-ETS CO2 allowances with associated risk for carbon leakage from Europe, the rapidly increased difficulty of exporting aluminium scrap from Europe to China, and modern society’s ever-increasing need for silicon metal. With SisAl, all these challenges are turned into new European opportunities.

Cheaper and more powerful sensors, together with big data analytics, offer an unprecedented opportunity to track machine-tool performance and health condition. However, manufacturers only spend 15% of their total maintenance costs on predictive (vs reactive or preventative) maintenance. The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%. The platform includes 4 modules: 1) a data acquisition module leveraging external sensors as well as sensors directly embedded in the machine tool components, 2) an artificial intelligence module combining physical models, statistical models and machine-learning algorithms able to track individual health condition and supporting a large range of assets and dynamic operating conditions, 3) a secure integration module connecting the platform to production planning and maintenance systems via a private cloud and providing additional safety, self-healing and self-learning capabilities and 4) a human interface module including production dashboards and augmented reality interfaces for facilitating maintenance tasks. The consortium includes 3 end-user factories, 3 machine-tool suppliers, 1 leading component supplier, 4 innovative SMEs, 3 research organizations and 3 academic institutions. Together, we will validate the platform in a broad spectrum of real-life industrial scenarios (low volume, high volume and continuous manufacturing). We will also demonstrate the direct impact of the platform on maintainability, availability, work safety and costs in order to document the results in detailed business cases for widespread industry dissemination and exploitation.

The emergence of the new coronavirus has meant a turning point, a radical change in many facets of our life and of course in the social perception of science and science communication too. During the crisis and for the first time in our era, a very large part of society really needed to rely and understand how science works and its outcomes, and they had to fight to find reliable and accurate sources of scientific information. The deafening noise of the first weeks was a sign that maybe scientist and their institutions have to communicate and listen better and that a closer relationship between science and society is still needed. In Spain, a survey carried out in April 2020 showed that healthcare professionals were the group that citizens rated most highly followed by scientists. In previous studies, such as Social Perception of Science 2018, it was already clear that the professions most highly valued by citizens were, in this order, doctors, scientists and teachers, over engineers, entrepreneurs, judges and journalists. Even so, a recent study certifies a 30% drop since 2000 in the number of enrolments in scientific and technical careers. It points out different causes for this drop and the main one seems to be the perception that it is not worth making the effort due to the imbalances in the job market. So, in this context, G-Night seeks to bring its research institutions to the whole population, to continue working and caring for a relationship that is strengthened every day. And for the first time, the main Galician centres and universities aim to do it together, reflecting the collaborative nature of science and knowledge generation. The Scientific Culture and Innovation Unit of UVIGO leads a project to bring together the 3 Galician universities, several research centres, museums, libraries and other science organizations and associations with the public to show the reality of research and science in different areas of knowledge

The development of high quality products and processes is essential for the future competitiveness of the European economy. In most key technology areas product development is increasingly based on simulation and optimization via mathematical models that allow to optimize design and functionality using free design parameters. Best performance of modelling, simulation and optimization (MSO) techniques is obtained by using a model hierarchy ranging from very fine to very coarse models obtained by model order reduction (MOR) techniques and to adapt the model and the methods to the user-defined requirements in accuracy and computational speed. ROMSOC will work towards this goal for high dimensional and coupled systems that describe different physical phenomena on different scales; it will derive a common framework for different industrial applications and train the next generation of researchers in this highly interdisciplinary field. It will focus on the three major methodologies: coupling methods, model reduction methods, and optimization methods, for industrial applications in well selected areas, such as optical and electronic systems, economic processes, and materials. ROMSOC will develop novel MSO techniques and associated software with adaptability to user-defined accuracy and efficiency needs in different scientific disciplines. It will transfer synergies between different industrial sectors, in particular for SMEs. To lift this common framework to a new qualitative level, a joint training programme will be developed which builds on the strengths of the academic and industrial partners and their strong history of academic/industrial cooperation. By delivering early-career training embedded in a cutting-edge research programme, ROMSOC will educate highly skilled interdisciplinary researchers in mathematical MSO that will become facilitators in the transfer of innovative concepts to industry. It will thus enhance the capacity of European research and development.