In the manufacturing sectors, the traditional planned maintenance approach is no longer viable, as it cannot cope with the ever-rising complexity of production systems. This pressing problem hurts industry’s profitability, and unplanned downtime costs industrial manufacturers €43 billion per year. This pressing problem has fuelled the growth of the predictive maintenance market. Currently, predictive maintenance solutions employ typical machine learning approaches based on monolithic rule-based predictions and require from the customer labelled data that correspond to defective machine states. This impedes the penetration of predictive maintenance in the industry. EnCORE is the fruit of 5 years of R&D to develop proprietary deep neural networks fit for predictive maintenance applications. Our solution uses best-in-class deep learning technology removing the overheads related with data preparation and enable the prediction of machine’s future condition using data that correspond to normal machine states. This is a game changing approach in the predictive maintenance industry. EnCORE is at TRL-6, with our software being validated at two different applications, (1) a compression moulding machine that produces plastic bottle enclosures/caps and (2) a cold forming machine that produces razor blades. Our target market will be the Food & Beverage and Consumer Goods industries targeting both OEMs of machinery and End-Users use such machinery. To take our product to the market, we will employ an hybrid business model using both direct sales and sales through industrial IoT platforms. EnCORE’s unique offering unlocks tremendous value for our customers; this will fuel the adoption of our solution by the industry. In the commercialisation period, we forecast cumulative profits of about €15 million with a strong Return on Investment (ROI) of €13 million. This will allow us to grow our workforce by 83 new employees, to meet the expected market demand for our breakthrough product.

CORE Innovation Centre (CORE) is a private non-profit research organisation, based in Greece and founded in 2021 as a full subsidiary of CORE Innovation Technology OE, focusing on Industry4.0 technologies, like machine learning, IoT, edge and cloud computing, Innovation Management and Communication. The vision of CORE is to become the biggest R&I centre in Southeastern Europe in the fields of Industry4.0. CORE aims to create an industrial ecosystem, composed of manufacturing companies and knowledge/research institutions, around the core principles of Industry 4.0 such as automation, zero-defect manufacturing, and flexible production based on a number of core technologies such as Machine Learning, Robotics, Big-Data and Digital Twins. This vision will be materialised through the development of Industry 4.0 Test Labs, which are practical environments where companies and research institutions develop, test and implement Industry 4.0 solutions under realistic conditions, as well as co-design the associated digital processes and new business models. The overall Twin4Twin goal is to raise the research profile of CORE as well as the research profile of its staff in the field of digital twin technologies. In parallel with the objectives, Twin4Twin’s concept approach is divided into four clusters: a. digital twins – scientific excellence b. research and innovation management upskilling, c. business development, and d. exploratory research project. To do that, CORE will run the twinning exercise with leading EU organisations, namely ITA from Spain (Reduced Order Models research in Digital Twins), SCCH from Austria (Big Data real time streaming technologies) and SSF from Switzerland (test and demo platform to be used as use case and provide the replication potential towards the Greek Smart Factory).Twin4Twin aims to reduce the disparity in production between Greece and top-class leading countries through knowledge transfer in smart manufacturing technologies.

The increasing energy demand and the depletion of fossil-fuel reserves, threatening our energy security and the environment, have aroused intense global concern. To mitigate this, the EU aims to become climate-neutral by 2050, by targeting at the next-generation of biofuels from non-land and non-food competing bio-wastes. Butanol (BuOH), heavier alcohols and hydrogen (H2), if produced from bio-ethanol, are promising advanced biofuels due to their high energy content, long shelf-life and, in case of BuOH, compatibility with the current engines and fuel distribution infrastructure. However, their production faces challenges due to the low yields and selectivities during ethanol reforming. GlaS-A-Fuels envisions a holistic approach to transform bio-ethanol to advanced biofuels employing recyclable and cooperative catalysts from earth-abundant elements. The concept is based on the engineering of a light-trapping and light-tuning photonic glass reactor, self-powered by a thermoelectric module, and tailored to amplify the effectiveness of photo-amplified single-atom catalysts. GlaS-A-Fuels aims to harness the full power of the light-activated carriers of photoactive supports by channeling this energy to the surface-exposed transition metal-cation single atom sites. There, via the effective coordination with the reactants and energy matching with their frontier orbitals, solar energy to fuel conversion can be maximized. Metal-metal and metal-support cooperativity, charge transfer phenomena and strongly polarized oxidations states can further contribute to the required enhanced catalytic performances and difficult-to-achieve key reaction intermediates. To develop efficient processes for the production of advanced biofuels, GlaS-A-Fuels will leverage in a concerted way the key expertise of five partners in materials science for solar and thermal energy harvesting, catalysis, laser technologies for tuning light-matter interactions, intelligent process-control systems.

DIONE proposes a close-to-market (TRL7) area-based direct payments monitoring toolbox that will address the forthcoming Modernised CAP regulation of using automated technologies to ensure more frequent, accurate and inexpensive compliance checks. In particular, DIONE will: (i) Capitalise on recent results of ESA’s SEN4CAP project that showcased the capability of Sentinel data to monitor the crop diversification rules. DIONE shall further integrate generated crop-type maps in a way directly exploitable by the paying agencies; (ii) Include in the analysis the so far neglected EFA types (fallow land of all sizes, buffer strips, hedges, trees), by making use of super-resolution technology that improves the 10-20m Sentinel resolution to an improved resolution range (5-10m). This is enabled through Machine-Learning (ML) based post-processing and data fusion of Copernicus DIAS-sourced data with targeted drone-obtained data. This aims to motivate the use of such EFAs over the –of ambiguous environmental impact- use of productive areas (nitrogen-fixing crops and catch crops). (iii) Complement the use of EO data with a system of reliable, ground-based geo-tagged photos, captured by the farmers that exploits (a) advances that allow for improved positional accuracy, (ii) low-footprint encryption techniques for improved data security and reliability and (iii) image detecting manipulation techniques (image forensics). The system will allow for an improved LC/LU annotation and ensure the process is untampered. (iv) Implement a Green Compliance toolbox, integrated with the paying agencies’ aforementioned tools. This will benefit from (a) low-cost spectral sensors measuring soil quality and assessing the status of land-degradation in the land parcels and (b) an ML-based inferencing system deployed on a larger scale (regional, national) to quantify the levels of some of the monitored parameters and consequently extract tangible environmental performance metrics for an entire region

By applying a circularity-by-design approach, the AMBIANCE project aims to develop new and advanced bio-based products, characterized by a high or total bio-based material content and taking into account the different alternatives for recirculation such as the different types of reuses, remanufacturing, recycling, biodegradation or energy recovery to enhance sustainable models. Such approach will be coupled to the optimization of the mechanical properties for particular applications, e.g. durability for outdoors urban furniture or sports and leisure products, taking into consideration the whole lifecycle. Special attention will be paid to the optimization of product manufacturing of bio-based materials, which will require tuning of material composition and processes (extrusion, large-scale additive manufacturing, compression moulding) for different bio-based materials. Besides, the use of Digital Twin technologies will enhance the development of materials and products and the remanufacturing of such bio-based goods, and it will optimize manufacturing processes and enhance production quality of such novel applications by leveraging IoT and Artificial Intelligence technologies. AMBIANCE's impact will be two-fold: firstly we will showcase disruptively innovative bio-based products in different sectors and manufacturing processes, while we will demonstrate applicability in daily-life products, starring green urban areas that will lead the transition to sustainability. The setting proposed by AMBIANCE will directly result in the creation of new jobs, based on new technical competences that will require specific training and upskilling, while the technical developments and findings will be used to contributing to the standardization of bio-based products and materials, which will guarantee an effective and wide adoption for making such products the new baseline of our lives.