The main objective of H2PlasmaRed is to develop hydrogen plasma smelting reduction (HPSR) technology for the reduction of iron ores and steelmaking sidestreams to meet the targets of the European Green Deal for reducing CO2 emissions and supporting the circular economy in the steel industry across Europe. Our ambition is to introduce a near CO2-free reduction process to support the goal of the Paris Agreement - a 90% reduction in the carbon intensity of steel production by 2050. To achieve this, H2PlasmaRed will develop HPSR from TRL5 to TRL7 by demonstrating the HPSR in a pilot-HPSR reactor (hundred-kilogram-scale) that is an integrated part of a steel plant, and in a pilot-scale DC electric arc furnace (5-ton scale) by retrofitting the existing furnace. The project's end goal is to establish a way to upscale the process from pilot-scale into industrial practice. To support this goal, the novel sensors and models developed and implemented in the project are used for HPSR process optimization from a reduction, resource, and energy efficiency standpoint.

Over the past 5 years, deep learning has exercised a tremendous and transformational effect on the field of computer vision. However, deep neural networks (DNNs) can only realize their full potential when applied in an end-to-end manner, i.e., when every stage of the processing pipeline is differentiable with respect to the network’s parameters, such that all of those parameters can be optimized together. Such end-to-end learning solutions are still rare for computer vision problems, in particular for dynamic visual scene understanding tasks. Moreover, feed-forward processing, as done in most DNN-based vision approaches, is only a tiny fraction of what the human brain can do. Feedback processes, temporal information processing, and memory mechanisms form an important part of our human scene understanding capabilities. Those mechanisms are currently underexplored in computer vision. The goal of this proposal is to remove this bottleneck and to design end-to-end deep learning approaches that can realize the full potential of DNNs for dynamic visual scene understanding. We will make use of the positive interactions and feedback processes between multiple vision modalities and combine them to work towards a common goal. In addition, we will impart deep learning approaches with a notion of what it means to move through a 3D world by incorporating temporal continuity constraints, as well as by developing novel deep associative and spatial memory mechanisms. The results of this research will enable deep neural networks to reach significantly improved dynamic scene understanding capabilities compared to today’s methods. This will have an immediate positive effect for applications in need for such capabilities, most notably for mobile robotics and intelligent vehicles.

The integration of reaction and downstream processing steps into a single unit is of central importance in order to achieve a new level of process intensification for catalytic driven and eco-friendly reaction systems. This disruptive technology concept has the ability to reduce the total energy consumption of large volume industrial processes by up to 78%. Additionally, emissions can be reduced by up to 90% To achieve this, HOMOGENEOUS catalysts are supported on membranes. Embedding the homogeneous catalysts in thin films of non-volatile ionic liquids (SILP technology) will maintain their catalytic abilities as in the homogeneous phase while the anchoring directly on the membrane ensures a most efficient separation. The new technology concept will be proven by two prominent large volume reaction types: a) Processes with undesired consecutive reactions like hydroformylation and b) Equilibrium driven reactions like water gas shift (WGS) reaction. These processes for bulk chemicals and bio energy applications have been chosen to demonstrate the high impact of the ROMEO technology in an industrial near environment. Nonetheless, it is a core task to also get a detailed understanding of the general processes on a molecular level for the different required functionalities. One achievement will therefore be to provide a modelling “tool-box” that can be applied to any other process in order to check the benefits of the ROMEO technology for a specific reaction in a short time. The ROMEO reactor methodology allows being highly flexible and adapting to both different process and volume requirements. An increase in production volume can then be achieved by a simple numbering up of reactor modules.

The decrease of CO2 & particulates emissions is a main challenge of the automotive sector. European OEMs and automotive manufacturers need new long term technologies, still to be implemented by 2030. Currently, hybrid powertrains are considered as the main trend to achieve clean and efficient vehicles. EAGLE project is to improve energy efficiency of road transport vehicles by developing an ultra-lean Spark Ignition gasoline engine, adapted to future electrified powertrains. This new concept using a conventional engine architecture will demonstrate more than 50% peak brake thermal efficiency while reducing particulate and NOx emissions. It will also reach real driving Euro 6 values with no conformity factor. This innovative approach will consequently support the achievement of long term fleet targets of 50 g/km CO2 by providing affordable hybrid solution. EAGLE will tackle several challenges focusing on: • Reducing engine thermal losses through a smart coating approach to lower volumetric specific heat capacity under 1.5 MJ/m3K • Reaching ultra-lean combustion (lambda > 2) with very low particulate (down to 10 nm) emission by innovative hydrogen boosting • Developing breakthrough ignition system for ultra-lean combustion • Investigating a close loop combustion control for extreme lean limit stabilization • Addressing and investigating NOx emissions reduction technologies based on a tailor made NOx storage catalyst and using H2 as a reducing agent for SCR. A strong engine modeling approach will allow to predict thermal and combustion performances to support development and assess engine performances prior to single and multi-cylinder test bench application. An interdisciplinary consortium made of nine partners from four different countries (France, Germany, Italy, Spain) will share its cutting-edge know-how in new combustion process, sensing, control, engine manufacturing, ignition system, simulation & modeling, advanced coating, as well as after-treatment systems.