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Middleware Development: Building a Unified Data Platform
Author: Maria Tassi, Nikos Gkevrekis
3rd July 2025
The CORE Innovation Centre technical team has released the alpha version of middleware platform that provides a unified solution for managing heterogeneous data flows across different data sources.
It is a flexible and scalable platform, which offers a robust foundation for seamless data ingestion, storage, processing, and secure access across diverse systems and demonstration sites. The development of this middleware platform is in line with CORE’s digital transformation mission, helping organisations accelerate their transition through cutting-edge research and technology development which addresses real-world barriers that hinder progress for many manufacturers, regardless their specific industry.
Architecture Overview
The middleware is structured around a layered architecture (see the figure below), which consists of four primary layers – Ingestion, Storage, Processing, and Consumption – all supported by Orchestration and Monitoring layers. These interconnected components ensure that the platform can handle a wide spectrum of data types while maintaining operational coherence and traceability.
Middleware architecture
Key features of the architecture
Multi-Source Data Ingestion: Designed to integrate heterogeneous data streams, the ingestion layer supports:
MQTT for real-time data
REST API for batched real time and historical data
File uploads (e.g. images, GIS) through a fileserver
It also supports ETL – Extract, Transform, Load – processes and performs data validation on entry to maintain data quality and consistency.
Versatile Storage: The storage layer is optimized for various data types:
large files
structured data
time-series data
Features like pagination and sorting enhance performance, especially for large-scale datasets.
Secure and Dynamic Data Access: The consumption layer exposes data via RESTful APIs, featuring:
Token-based authentication
Role-based authorisation
This way, users can query real time, historical records and batched data that we ingested from real time sources, specify time ranges, and retrieve files in original or compressed formats. The system also supports dynamic endpoints tailored to specific organizations or devices.
Interoperability and Integration: The platform is built to work across multiple sources and demonstration sites
Scalability and Extensibility: As an alpha release, the architecture anticipates future enhancements including real-time processing, advanced analytics modules, and tighter integration with external systems, supporting the evolving needs of diverse pilot sites.
Ingestion Layer
The development process began with the ingestion layer, which serves as the gateway for all incoming data. Designed with flexibility, this layer can receive data from real-time sources such as MQTT, scheduled or historical data via APIs, and large files like images and geospatial datasets through a fileserver. This fileserver was developed to support large document handling, enabling users to upload, download, and manage files in their original formats, in order to accommodate diverse data requirements from real-time data to large-scale datasets. In addition to managing data intake, the ingestion layer plays a key role in validating incoming information and preparing it for further use. It supports ETL operations, which ensure that data is harmonised, transformed when necessary, and made ready for further analysis or storage.
Storage Layer
In parallel with the ingestion layer, significant progress was made on the storage layer. This layer is designed to efficiently store the wide variety of data collected by the system. It integrates multiple storage technologies like S3 buckets, PostgreSQL, TimescaleDB etc. for general data storage, for handling files from the fileserver, and for managing time-series data ensuring optimal performance and scalability.
Consumption Layer
Development has also begun on the consumption layer, which is responsible for enabling secure access to the stored data. This layer currently provides REST API that are protected by token-based authentication and role-based authorization, ensuring that only authorized users can access sensitive information. Users can query bached real time data, historical data by requesting specific time ranges, and retrieve files either in their original form or in compressed formats and define pagination and sorting which enhance the speed and efficiency of data retrieval. Additionally, the consumption layer supports dynamic endpoint creation based on organizational structures or specific device IDs, allowing it to adapt easily to the varying needs of different demo sites and stakeholders.
Responding to real-life challenges
The system's complexity presented various challenges during development. Managing a variety of input formats, including real-time IoT data, historical API feeds, and large unstructured files, required the development of a flexible and adaptable ingestion system which can process heterogenous types of data. Ensuring data quality across many formats and sources necessitated the development of robust ETL methods as well as versatile and dynamic schema validations.
Another challenge was securyity, as designing a secure system with token-based authentication and role-based authorisation presented difficulties in multi-site, multi-user scenarios. To balance flexibility with system performance, especially for large-scale time-series data and file management, storage solutions have to be carefully selected and configured.
Furthermore, developing and maintaining dynamic endpoints able to consume data as they are being ingested required a careful and complicated database schema and management. Last but not least, deploying, managing and scaling numerous different data ingestion and consumption services requires the development and usage of complex custom orchestrating and monitoring tools.
Conclusions
The release of alpha version of the middleware platform marks a significant step toward a flexible and robust solution for managing heterogeneous data. With its layered architecture, it supports seamless data ingestion from real-time data flows, APIs, and large files, while ensuring efficient storage, validation, and secure access. Features such as ETL processing, dynamic endpoints, and multilevel authentication enable adaptability, interoperability, and data integrity across diverse sources.
The middleware platform has a substantial market impact, because it enables interoperable data exchange across several sources, boosting collaboration in various fields such as manufacturing, climate resilience, and industrial processes. This interoperability accelerates digital transformation by combining real-time, historical, and large-format data to create a secure, scalable infrastructure that improves decision-making and operational efficiency.
Designed to manage complex, multi-site systems, it provides dynamic endpoints and role-based access while establishing the groundwork for future features such as real-time analytics and AI integration.
Elements of a secure and interoperable middleware approach have been explored and developed within two of our Horizon EU projects; CARDIMED, which focuses on boosting Mediterranean climate resilience, and MASTERMINE, which focuses on building a digitalized copy of real-world mines through an Industrial Metaverse approach.
The newly-released middleware platform is aligned with our CORE mission of accelerating digital transformation through cutting-edge research and technology development, especially in data interoperability, artificial intelligence, and industrial digitization and demonstrates our dedication to developing smart, adaptive, and future-ready solutions that address real-world difficulties across industries.
The alpha version lays a strong foundation for future enhancements, including advanced analytics, real-time processing, and broader system integration, positioning the middleware as a key enabler in modern data ecosystems.
Data-Driven Digital Shadows for Process Manufacturing
Author: Iason Tzanetatos
26th June 2025
Circular TwAIn is a Horizon EU project which aims to research, develop, validate, and exploit a novel AI platform for circular manufacturing value chains. This AI platform can support the development of interoperable circular twins for end-to-end sustainability.
Within the scope of our activities in the Circular TwAIn project, CORE IC is responsible for the design, development and deployment of a data-driven Digital Shadow for an industrial process, such as that of a petrochemical plant (the Circular TwAIN end user, SOCAR).
In process manufacturing, particularly in complex and highly regulated industries like petrochemicals, the ability to monitor, analyse, and optimise operations in real time is essential for maintaining efficiency and competitiveness. The integration of advanced digital technologies is reshaping traditional operations, enhancing performance, and driving innovation. To be able to realise this model, we have utilised historical operational data from our partners at SOCAR, that they have kindly shared with the consortium.
Architectural Design of Process Digital Shadow
Working with our partners at TEKNOPAR and SOCAR, we defined their requirements from the technology and identified the following key objectives:
1. Perform anomaly detection on real-time sensorial data, that depict the current conditions of the plant
2. Predict what the sensor readings will be in a 10-minute horizon (i.e., predict short-term future state of the plant)
3. Perform anomaly detection on the forecasts of the plant.
We started the development of a Digital Shadow for a manufacturing process by identifying the involved assets of the production line. Since we are dealing with a process where the involved assets interact with one another, the outputs of some of the assets are considered as inputs to other assets down the line.
After the involved assets and their interactions had been mapped, we moved to the mapping of the sensorial inputs/outputs of each asset. Relevant information such as SCADA schemes were used to determine the sensors that are considered as inputs and outputs of each machine.
Data Augmentation Techniques – Asphalt Use case
To successfully identify any anomalous conditions for each sensorial signal, irrespective of the input/output characterization of the involved sensors, we monitored each signal individually.
We proceeded with training an Autoencoder-like Deep Learning model, to identify anomalous conditions on a per sensor level, meaning that the model examines the data point of each sensor individually.
Autoencoder Neural Network Architecture
As depicted in the figure above, an Autoencoder model comprises three main components:
1. The Encoder, where the input information is compressed
2. The Bottleneck layer, where a compressed low dimensional representation of the input is determined by the model
3. The Decoder, which reconstructs the input relying only on information retrieved by the Bottleneck layer
By training an Autoencoder model with high quality, normally characterised operational data, we essentially have a model that can identify significant deviations on the operations of the involved assets.
However, since this use-case is particularly complex, niche models such as Reservoir Computing deep learning models. This family of models is best suited for time series data with complex patterns, similarly to the operational data of a manufacturing process.
From: Quantum reservoir computing implementation on coherently coupled quantum oscillators
By adopting the approach of the Autoencoder model, we formulate the problem in the exact manner, only we swap models and use a Reservoir Computing model.
Forecasting
To proceed with forecasting 10-minutes ahead of the manufacturing process, our model needs to follow the same interactions of the assets as in the actual plant.
Production flow on Petrochemical line
The process comprises three main components, a Reactor, an Absorber and a Stripper. Each asset interacts back and forth with each other, and these interactions must be replicated in the digital realm as well.
A Digital Shadow has been developed for each physical asset. We utilised the same family of Deep Learning models to perform forecasting, by switching the objective of the models. As a final step, we connected each model by following the physical domain, as previously mentioned.
Through working with our partners, our technical team managed to successfully implement a Process Digital Shadow that achieves real-time anomaly detection, forecasting 10 minutes ahead, and identifies forecasted data points as normal or abnormal, offering the end-user an early warning system.
The s-X-AIPI project has concluded
Author: Vassia Lazaraki, Athanasia Sakavara, Nikos Makris, Clio Drimala
24th June 2025
The s-X-AIPI project aspired to transform the EU process and manufacturing industries by developing an innovative, open-source toolset of trustworthy self-X AI technologies. These AI systems are designed to operate with minimal human intervention, continuously self-improve to boost agility, resilience and sustainability throughout the product and process lifecycle.
s-X-AIPI supports industrial workers with smarter, faster decision-making, while promotes integration into a circular manufacturing economy. Utilising innovative AI tools enhances design, development, operation and monitoring of plants, products and value chains.
s-X-AIPI demonstrated in four industrial sectors: Asphalt, Steel, Aluminum, and Pharmaceuticals, showcasing a portfolio of trustworthy AI technologies like datasets, AI models, applications, which integrated into an open source toolset. Key components of this toolset include an AI data pipeline with automatic computing capabilities, an autonomic manager based on MAPE-K models, that supports Human In The Loop, as well as several AI systems based on continuous self-optimiσation, self-configuration, self-healing and self-protection.
Launched in May 2022 with 14 partners from 6 different conuntries, the 3-year project concluded in April 2025. An online Final Review will be conducted in the summer of 2025, highlighting its significant achievents during the project’s lifespan.
ADAPT AI-Powered Anomaly Detection – Steel Use Case
CORE IC developed ADAPT – Active Detection and Anomaly Processing with smart Thresholds - a cutting-edge anomaly detection system designed to enhance operational oversight in steel manufacturing, through state-of-the-art machine learning and automation.
Powered by a Conditional Variational Autoencoder (cVAE), ADAPT continuously monitors process data—including scrap input, in-process metrics, and product compositions—to identify deviations and present them to process experts.
The base model was trained in an unsupervised manner on large volumes of historical, unlabeled production data, an ideal approach for industrial environments where labeled anomalies are rare and difficult to obtain. A Bayesian optimisation framework was integrated into the training pipeline, to allow data-driven hyperparameter tuning. Finally, an explainability module was introduced to the inference for transparency and user trust: for each detected anomaly, ADAPT highlights the top contributing features, enabling experts to quickly understand root causes and take informed corrective action.
ADAPT’s strength lies in its ability to continuously evolve. It supports two complementary mechanisms for refining the base model:
semi-supervised learning loop, which incorporates expert feedback on the identified anomalies, through a Human-in-the-Loop (HITL) workflow. ADAPT’s active learning framework minimises the need for constant human intervention, while still leveraging expert input where it adds the most value—driving continuous operational decision support.
unsupervised adaptation strategy on new, unseen data that addresses both data drift and concept drift over time.
These fine-tuning processes, along with performance monitoring, model redeployment, and user feedback tracking, are fully automated within ADAPT’s MLOps pipeline. The system also logs and manages historical anomaly data, for tracking patterns, comparing model behavior over time, and supporting process audits or improvement initiatives. Experiment tracking, version control, and metadata management ensure that every model iteration is traceable, reproducible, and aligned with production requirements.
ADAPT End-to-end pipeline for robust, adaptive Anomaly Detection
Data Augmentation Techniques – Asphalt Use case
Software developments in the Asphalt Use Case (UC) faced a key limitation: the scarcity of high-quality laboratory test data. Although the data spans long operational periods, the overall volume remains low, making it difficult to train effective AI models for analysing asphalt behaviour and predicting performance outcomes. This lack of data particularly impacts supervised learning approaches, leading to class imbalance, reduced model robustness, and constrained generalisation. To address this challenge CORE IC developed a three-stage data augmentation pipeline, illustrated below, that expanded the original dataset (~500 rows) to approximately 103.
Three stages of our Data Augmentation Technique
The first stage focused on imputing missing values using the K-Nearest Neighbors (KNN) algorithm, selecting the five closest data points to estimate missing entries. In the second stage, Gaussian noise was added to the dataset to introduce variability and promote model robustness, while preserving the data's underlying structure. The final stage involved experimentation with three generative AI models—Variational Autoencoder (VAE), Denoising Autoencoder (DAE), and ReaLTabFormer—each used to generate synthetic records that enriched the dataset. These enhanced datasets were then evaluated for their impact on predictive performance. Among them, the dataset generated by the VAE method emerged as the most effective, significantly enhancing the model’s accuracy and predictive performance.
Dissemination, Communication and Exploitation Activities
We are just a few days away since the project wrapped up and cannot omit reflecting back on some valuable dissemination and communication (D&C) achievements. Over the past 3-years, CORE IC devised and led the dissemination and communication strategy, working hand-in-hand with the entire consortium to maximise the project's visibility and impact.
The s-X-AIPI team participated in 24 high-impact events presenting their findings and organised the “Transforming Process Industries with AI” project-dedicated concluding event on 9 April, 2025 in Belgrade. These events offered remarkable opportunities for an extensive audience reach across significant target groups worldwide including professionals from research and academia, industry, IT, software, and technology, business consulting, EU institutions, national/regional, and local authorities, as well as policy making, investors and financial stakeholders, specific end user communities, association representatives and the general public and media.
Beyond that, consortium members generated 8 open-access scientific articles (6 already published and 2 currently in the publication process), an important legacy of the project the full list of which can be found on the project website or the ZENODO repository. The AI4SAM Cluster was also formed with two more EU funded projects – AIDEAS and Circular TwAIn - to expand the project’s impact beyond individual efforts. This cross-project collaboration ranged from joint event participations in major conferences, targeted webinars and the s-X-AIPI final event to collaboration on digital communication activities to amplify each project’s outreach.
The s-X-AIPI website, designed and maintained by CORE IC, will continue as a central hub for useful information and resources. On the site, visitors can learn more about the project’s final results, important research activities performed and landmark research news of each project phase through 7 press-releases, 9 newsletter issues, videos, open-access scientific papers, public deliverables and training courses.
Additional strategic digital communication efforts included the creation of 13 videos - all available on YouTube - to convey the s-X-AIPI concept and remarkable achievements in a more engaging manner than text-based content. The project also shaped valuable online communities on LinkedInX, significantly expanding its reach, another reflection of the overall effectiveness of the D&C strategy.
For the Exploitable Results (ERs) that are closer to the market, CORE IC used its’ Profit Simulation Tool (PST) to forecast the financial revenues during the post-project commercialisation period. CORE’s PST offers a structured approach to support commercialisation planning by combining strategic business insights with market data. The financial forecasts provided by the PST were integrated effectively into the project’s exploitation strategy.
Further support from the CORE IC team
SIDENOR, a steel making facility in Spain, worked in s-X-AIPI with a focus on optimising scrap usage, especially addressing challenges from contaminants such as copper commonly found in lower-quality external scrap. Their main goal is to produce high-quality steel, prevent downstream surface defects, and minimise energy consumption in the Electric Arc Furnace (EAF) melting process. CORE IC contributed to this effort by supporting partners BFI and MSI through the development of anomaly detection software, as part of the overall solution.
Additionally, CORE IC was involved in the Asphalt use case. The aim of this use case was to target circularity of the value chain, from quarry to road, by enhancing quality control of feedstock (aggregates, bitumen, recycled asphalt), improving the overall sustainability of the production process (including asphalt paving) and the quality of final product (asphalt mix). Partners CARTIF, DEUSER, and EIFFAGE, also working on this use case, leveraged the augmented data generated by CORE IC to enhance model performance.
CORE Group at the 2025 International Smart Factory Summit
Author: Alexandros Patrikios
19th June 2025
The 6th International Smart Factory Summit took place earlier this month, bringing together smart factory innovators from all over the world to explore the future of smart manufacturing.
During the Summit, Dr. Nikos Kyriakoulis, CORE Group Co-Founder and Managing Partner, participated in a pitching session for the Greek Smart Factory, our CORE initiative facilitated through the Twin4Twin project.
The Summit
Hosted by our Twin4Twin partners Swiss Smart Factory (SSF), the Summit has served as a global platform and an annual gathering for the smart factory community to discuss how these ecosystems can transform industrial operations.
Under the theme “Deep Tech Smart Factory – Uniting Humans, AI, Robots & Processes”, ISFS25 focused on the economic and societal impacts of emerging deep technologies and their role in reshaping manufacturing. The three-day event was geared toward international decision-makers from both the public and private sectors, and brought together a global community of experts from Europe, Asia, Africa, South and North America.
The Greek Smart Factory pitching session
Dr. Nikos Kyriakoulis took the stage to present the Greek Smart Factory, a test and demo platform that brings together manufacturers, tech providers, and academia to drive innovation in real-world industrial settings. During his talk, he presented the strengths of the GSF, as well as key steps forward towards the bigger picture of bringing the initiative to life.
If you would like to know more about the GSF initiative or express your interest, you can fill the form available here and our team will reach out.
Stefanos Kokkorikos, CORE Group Co-Founder and Managing Partner, was also in attendance. Stefanos Kokkorikos is also the Project Coordinator for the Twin4Twin Project, an EU Horizon Widening project and a big milestone for CORE Group and CORE IC. You can find more information on the Twin4Twin project website.
Our warmest thanks to the Swiss Smart Factory for the ongoing collaboration.
Optimising Battery Performance and RUL with CE-DSS
Author: Christina Vlassi
June 3rd 2025
Our tech team designed a Decision Support System (DSS) solution that enhances battery usage decision making, paired with an innovative Remaining Useful Life (RUL) estimation module, designed for smarter analysis and long-term optimisation.
We developed the solution as part of the DaCapo project, for end-user Fairphone. DaCapo aims to create human-centric digital tools and services which improve the adoption of Circular Economy (CE) strategies throughout manufacturing value chains and product lifecycles. The project has been ongoing for 2 and a half years and comprises 15 partners across 10 countries with a budget of 5.99 million euros.
Fairphone is a key DaCapo partner, founded in 2013 to address the “make-use-dispose” trend through its focus on modular smartphones that are durable and easy to repair.
How our solution works
Our aim was to develop a DSS that enhances decision-making around battery usage, through its pairing with a RUL estimation module – all of which is accessible to users through a dedicated web app. When users access the app, they get instant access to a battery report preview for all their devices.
From there, the system splits into its two core functionalities: RUL Estimation and Forecasting & Suggestions.
Remaining Useful Life Estimation
In this module, users can see battery capacity loss based on mathematical models that map out capacity degradation over time. These insights reveal how much useful life remains in the battery — giving users a clear view of their device’s condition.
Where the DSS shines is through the provision of personalised guidance. By analysing app usage patterns, the system identifies the impact of each app on battery degradation. Through data analysis, we offer custom recommendations to help users understand and adjust usage patterns that are draining their battery life faster than necessary.
Forecasting & Suggestions
Harnessing the power of machine learning, the system predicts RUL 10 days ahead — tailored specifically to the user’s habits. With these predictions set, the web app goes on to offer actionable tips for longer battery life, with suggestions on the temperature control, charging patterns and usage patterns. When users follow these tips, the RUL of their device is improved, which is reflected visually on the app, showing the tangible benefits of informed action and encouraging sustainability-oriented behaviour.
Towards a Greener Future
By helping users extend the battery life of their devices, our system supports circular economy principles, reducing electronic waste and promoting sustainability. Users are empowered to optimise their battery usage, minimise capacity loss and make smarter, eco-conscious choices – one device at a time.
Multi‑Level Communication and Computation Middleware in MODUL4R
Author: Maria Tassi
May 30th 2025
The MODUL4R project is transforming industrial manufacturing by creating adaptable, resilient, and reconfigurable production systems. The backbone for this transformation is a distributed control framework for modular "Plug & Produce" (PnP) systems, the development of which was successfully completed in April 2025.
CORE IC led these research activities and developed the Multi‑level Communication and Computation Middleware (MCCM) - a fundamental innovation in MODUL4R - that bridges the gap between Cyber‑Physical Systems (CPS) and modular data exchange frameworks, allowing for smooth interoperability across edge, fog, and cloud layers.
The Role of MCCM in Modular Manufacturing
The MCCM was developed to tackle the challenges of modern manufacturing where agility, real‑time decision‑making, and scalability are essential, optimizing industrial operations through dynamic task distribution. By integrating edge, fog, and cloud computing, the MCCM ensures efficient data processing, low latency communication, and dynamic resource allocation. Its architecture is in line with the RAMI 4.0 reference model, supporting Industry 4.0 standards and enabling vendor‑agnostic communication.
Some of the key features of MCCM include:
Hybrid Computing Platform
Edge Layer: Serves as the entry point to the asset layer, ensuring low latency communication and direct interaction with physical systems, enabling real‑time interaction with shopfloor devices.
Fog Layer: Acts as an intermediary for intensive computation, edge orchestration, and near real‑time execution, minimising the latency for critical decision making while reducing cloud dependency.
Cloud Layer: Provides security, scalability, and robustness while enabling external communication with third‑party services.
Dynamic Reconfiguration
Enables real‑time adjustments to workflows, ensuring adaptability to changing production demands.
Supports Infrastructure‑as‑a‑Service (IaaS), allowing third‑party applications to be deployed via containerization (e.g., using Kubernetes).
Service‑Oriented Architecture
Facilitates modular deployment of microservices, ensuring flexibility and scalability.
Uses MQTT, REST APIs or seamless data exchange between layers.
Orchestration Across Layers
The Orchestration Controller offers dynamic service allocation, across different levels, to optimize resource usage across different levels optimizing resource usage.
Enables multi‑cluster management, ensuring efficient workload distribution.
Orchestration within the MODUL4R hybrid computation environment
In more detail, MCCM establishes a service‑oriented, hybrid computation platform designed to orchestrate communication and computation within Cyber‑Physical Systems of Systems (CPSoS) networks. On the shopfloor, data generated by individual CPS components is acquired through industrial communication protocols such as OPC‑UA at the edge layer. This data is then transmitted via MQTT message brokers to corresponding MODUL4R services, where it is processed.
These services, along with the brokers, are typically deployed across the fog and cloud layers, depending on latency requirements and computational needs. MCCM facilitates seamless deployment of services and brokers across the appropriate layers and physical locations, enabling modularity and adaptability. This architecture ensures that each system component can operate on the most suitable computational resource, enhancing efficiency, reducing latency, and supporting scalable system operation.
By enabling dynamic orchestration and real‑time reconfiguration, MCCM provides a flexible infrastructure that supports near real‑time production optimization. The fog layer plays a critical role in enabling low‑latency data processing and rapid decision‑making, essential for responsive manufacturing systems.
Furthermore, MCCM supports third‑party application deployment in an Infrastructure‑as‑a‑Service (IaaS) model using containerization technologies such as Docker and Kubernetes. This approach not only enhances scalability and flexibility but also allows for real‑time system updates and adjustments without production downtime. To maintain system integrity and continuity, MCCM incorporates version control and traceability mechanisms, allowing manufacturers to roll back or update services and algorithms as needed—ensuring both operational stability and adaptability.
Implementation in MODUL4R Use Cases
The MCCM has been successfully deployed across MODUL4R’s pilot cases, demonstrating its versatility and impact:
FFT Use Case: The Quality Check station transmits capacitor inspection data via MQTT to the fog layer, where it is processed and forwarded to the cloud for analytics.
SSF Use Case: A soldering production system uses the MCCM to synchronize data from multiple stations, ensuring real‑time quality monitoring and control.
EMO Use Case: A CNC milling machine streams sensor data through MCCM to the cloud for predictive maintenance analytics.
NECO Use Case: MCCM orchestrates robotic arm coordination and metrology data.
Benefits for the Industry
The MCCM delivers transformative advantages for manufacturers:
Reduced Latency: Fog computing minimizes delays for critical decision‑making.
Scalability: Containerized services allow easy expansion to meet production needs.
Interoperability: Standardized protocols such as MQTT ensure compatibility with legacy and modern systems.
Resilience: Dynamic reconfiguration enhances system robustness against disruptions.
Security: Frameworks and tools such as cloud‑message‑brokers (Kafka, MQTT) and GAIA‑X policies ensure secure data management & distribution, all the way from edge sensors to cloud services
Conclusions
The MCCM delivers transformative advantages for manufacturers, by improving responsiveness, scalability, interoperability, and robustness in production systems. Using fog computing, MCCM minimizes latency through localized data processing, allowing for quicker, real time decision‑making.
Its support for containerized services enables rapid reconfiguration and seamless deployment, making it simple to grow operations as needed. Standardised protocols such as MQTT and REST‑APIs enable interoperability with both legacy and modern systems, whereas dynamic reconfiguration capabilities improve system resiliency, allowing operations to respond easily to disturbances or changing production requirements.
The Multi‑level Communication & Computation Middleware is a key component of the MODUL4R project, allowing for seamless integration of distributed control systems, real‑time analytics, and modular manufacturing workflows. By connecting edge, fog, and cloud layers, the MCCM enables manufacturers to achieve flexible, efficient, and sustainable operations, paving the way for the factories of the future.
3D Simulation‑driven optimisation for smart manufacturing lines
The Swiss Smart Factory use‑case
Author: Pantelis Papachristou
May 26th 2025
The latest demo by CORE Group’s technical team showcases a groundbreaking approach to drive digital transformation of manufacturing lines, using 3D simulation‑driven optimisation.
In collaboration with the Swiss Smart Factory (SSF) in Switzerland and using software developed by Visual Components, our team created a demo that shows how 3D simulations and data analytics can address critical bottlenecks and enhance overall efficiency and productivity in complex manufacturing operations.
3D simulation‑driven optimisation for smarter manufacturing
3D simulation is a powerful approach that uses advanced simulation software to create detailed virtual 3D models of production systems. These realistic virtual representations (3D scenes) accurately mirror the real world, including not only the physical geometry of objects and structures but also their texture, colour, lighting, and other visual properties.
In the context of manufacturing, 3D simulations are used to replicate entire production lines, individual machines, and workflows in a virtual environment. This method enables teams to identify inefficiencies and bottlenecks in the production line, optimise workflows, and test improvements without interrupting real‑world operations and risking downtime or disruption to actual production.
Key Features of the demo
The 3D simulation‑driven demo was developed in collaboration with the SSF, using simulation software developed by Visual Components - partnerships which our team has secured through the Twin4Twin and Modul4r Horizon EU projects. The demo was initially showcased during CORE Innovation Days, Greece’s first Industry 4.0 conference organised and hosted by CORE Group, with a follow‑up demonstration at CORE Group’s Beyond Expo booth.
In our demo, we focused on the SSF’s production line for the F330 drone, which includes several automated and robotic‑enhanced stations, such as a 3D printing farm for the drone’s blades, assembly and packaging stations, and a warehouse. By analysing the flow of components and monitoring machine utilisation, we identified areas for improvement and tested various optimisation strategies to boost overall throughput and efficiency.
3D simulation of the production line: Using the Visual Components simulation software, a virtual model of SSF’s production line was developed, including individual workstations and the transition of components (e.g. through Automated Guided Vehicles). To obtain a clear view of the production line’s behavior and establish a baseline for its performance, virtual sensors were integrated in each station to measure its cycle time, utilisation and throughput over time.
Data‑driven analysis: Leveraging data from each station, provided the foundation for identifying bottlenecks and inefficiencies in SSF’s production line that slowed down production flow. Through a dedicated data analysis, we pinpointed the root causes of these bottlenecks, highlighting the problematic areas which are keen to potential improvements and refining.
Optimisation scenarios: Based on the data analysis results, we tested four targeted optimisation scenarios, to address the observed bottlenecks. For example, we introduced intermediate buffer storage systems, expanded the capacity of particular workstations and added extra stations. Running the 3D simulations on these different scenarios allowed us to compare the optimisation results with the baseline, quantifying the simulated improvements in terms of overall production rate. Interestingly, the results were not always straightforward, as adding extra stations can also create new bottlenecks elsewhere in the line, leading to a decrease in productivity. Such unexpected results underscore the need for simulations in complex manufacturing environments, where interactions between different workstations can have non‑linear and counterintuitive effects.
Final report: Finally, we created a report that juxtaposes the cost for implementing each optimisation scenario with the induced improvement in production rate. This analysis was crucial in understanding the trade‑offs between implementing these optimisation strategies and the tangible benefits they provided. Moreover, this report serves as the starting point for deriving a more detailed ROI analysis. By inserting specific financial metrics, such as production costs and profit margins, manufacturers can calculate the potential financial impact of each optimisation scenario.
The Future of Manufacturing: Smarter, Faster, More Efficient
Our demonstration underscored the potential of 3D simulation‑driven optimisation to revolutionise manufacturing processes. By combining simulations with data analytics and photorealistic 3D models, manufacturers can gain deep insights into their operations, experiment with different optimisation scenarios, and implement improvements without disrupting production.
This approach not only helps identify and solve bottlenecks but also enables manufacturers to make smarter, data‑driven decisions that lead to improved efficiency, reduced downtime, and increased productivity. In this case, manufacturers can test and refine their processes, ensuring that their production lines are always operating at peak performance.
As industries continue to embrace digitalisation, 3D simulation‑driven optimisation will play an increasingly important role in shaping the future of manufacturing.
The ability to simulate, analyse, and optimise processes before implementing changes – “testing before investing” – offers a significant competitive advantage, allowing manufacturers to stay ahead of the curve and continue innovating in an ever‑evolving market.
The DiG_IT project has reached its conclusion
Author: Valia Iliopoulou
6th May 2025
After 4 and a half years, the DiG_IT project, which aimed at the transition to the Sustainable Digital Mine of the Future, is concluded. The main goal of the project was to address the needs of the mining industry to move forward towards a sustainable use of resources while keeping people and environment at the forefront of their priorities.
To this end, our consortium built an Industrial Internet of Things platform (IIoTp) which collects data from the mining industry (from humans, machines, environment and market) and transforms them into knowledge and actions. The aim of our IIoTp was to improve worker health and safety, making operations more efficient and minimising the environmental impact of mining.
The CORE team had a broad role in the project and contributed to the development of key components of the platform.
Safety Toolbox: Biosignal Analytics and Anomaly Detection
CORE was responsible for designing two out of the three components of the Intelligent safety toolbox that provides insights and supports the prevention of hazardous situations for people’s health in the mining field. The first component is the Biosignal Analytics & Anomaly Detection agent that is part of the Safety monitoring system of the Decision Support System (DSS).
The Biosignal Analytics component is a cloud agent responsible for monitoring and detecting changes in the health state of the individuals that work inside the mines. The system utilises the biometrical data from the smart garments and pairs them with Anomaly Detection models that operate in real-time, to detect any possible alerting states in the health of the miners.
Safety Toolbox: Air-quality smart monitoring and forecasting
The second component of the safety toolbox is the Air-quality smart monitoring and Forecasting agent, that is part of the Environmental and Safety monitoring system of the DSS. The agent is responsible for providing predictions of the air quality KPIs, meaning forecasting how specific air-quality substances are going to progress in the future.
This is useful for safety reasons, like notifying that a section of the mine should be evacuated when a dangerous substance exceeds the accepted limits. The development of the agent is based on Multi-Variate Neural Networks that are trained from data gathered from sensors that are deployed inside the mines.
Examples of actual and forecasted values for NO2
Analysis of target variables per hour
Predictive Operation System I
One of our team’s primary roles was the development of a Predictive Operation System that utilises AI architectures. The system consisted of a forecasting agent that predicts the consumption of an individual asset. The knowledge of anticipated consumption is critical for planning of the field operations and optimising the industrial processes.
Our team relied on COREbeat, our end-to-end predictive maintenance platform, to assist Marini Marmi, a historic marble quarry in the north of italy, with the operation of one of their critical assets. COREbeat was installed on an electrical supplied milling machine utilized for cutting though marble cubes producing marble slices. During the project, the operators received critical warnings from COREbeat. CORE team investigated and indicated the origin of the fault to Marini operators, who decided to halt the machine's operation and placed it in maintenance mode, preventing further damage to the machine.
More information on how COREbeat assisted the staff at Marini can be found here.
Predictive Operation System II
CORE was also primarily responsible for a Predictive Maintenance system that utilises Machine Learning methods and techniques. The system was designed to focus on individual assets, enabling the assessment of their overall health and the prediction of their future states in real-time. The assets selected for predictive maintenance were paired with Anomaly Detection models, specifically designed for the asset type, to maintain input consistency between applications. The goal was to create alerts that can provide early warnings to users about possible failures of the operating equipment.
Anomaly scores and anomaly thresholds of the test set
Commercialisation phase
To ensure successful commercialization, CORE’s dedicated team developed an Innovation Strategy, focusing on clear value propositions and competition mapping. The main findings show that European Economy will need to multiply its production of critical raw materials, due to the increasing digitalisation of the economy and use of renewables. Therefore, AI solutions are obligatory, if mining industry companies want to optimise operations as well as offer environmental protection but also health and safety to their employees.
Additionally, CORE developed business models for two key innovations introduced by the project, the DiG_IT IoT Platform, for mining operations optimisation, online measurements and failure predictions, and the Dig_IT Smart Garment, for improved safety and reduced risk of accidents and injuries, the two innovations with the highest TRL exposed in real conditions.
The InBlanc Project has kicked off
Author: Clio Drimala
April 24th 2025
The INBLANC research project has officially kicked off, bringing together professionals around Europe to Delft, the Netherlands for its official launch this past February.
The project aims to transform data usage in the building and construction sector by creating an open ecosystem that will maximise the value of building data across its lifecycle.
Diving into building data
Buildings generate vast amounts of data, yet much remains scattered and underutilized. To fully capitalize upon this untapped resource, INBLANC aspires to develop an open ecosystem that will transform building lifecycle data into actionable insights. Access to rich building information can support smarter decision-making for building owners and facility managers and drive value across the entire value chain. The project developments will include a cost-efficient data accumulation framework, a Building Digital Logbook consolidating building information, structured databases connected to European data spaces, and a suite of high-added value tools and services.
To help understand and improve building efficiency, INBLANC will focus on five key metrics:
Energy – Efficiency, consumption, and performance
Human – Health, occupant comfort, and well-being
Environment – Emissions, materials, and sustainability
Economy – Cost-effectiveness, value over time and resilience
Resilience – How buildings adapt to unexpected disruptions and stress
Using a nexus approach, INBLANC will model how these indicators influence each other, creating a full-picture view of building performance and identifying areas for improvement. All this data and analysis will feed into a suite of digital tools and services designed to help key stakeholders take action. Whether it is planning a renovation, improving energy consumption, enhancing indoor environmental health, or managing a property more efficiently, INBLANC will turn complex data into clear, practical steps.
CORE Innovation Centre’s Role
CORE IC will bring deep expertise to key aspects of the project:
Sustainable Renovations and Smarter Energy Use: CORE IC will play a key role in INBLANC, leading research on “Service toolset integration for capitalising Building Lifecycle Data” and “Energy Management Services”. This includes the creation of tools designed to optimise renewable energy investments and enable real-time energy management across buildings.
The team will also contribute to map financing instruments for deep renovations and contribute to nexus modeling with bio-physical and cyber-physical models. CORE IC’s efforts will also support tool integration within the project’s data model and focus on AI-driven analytics to improve decision-making.
In addition, CORE IC will be involved in a broad set of tasks, including data collection, sustainability roadmaps, decision support systems, and large-scale performance gap analysis. Through advanced monitoring and optimization strategies, CORE IC will help drive more efficient, sustainable renovations and smarter energy use.
Dissemination, Communication and Innovation Management: CORE IC will also lead all strategic communications to increase project awareness and build INBLANC's reputation across crucial target groups, from key specialists and stakeholders to broader audiences. This will ensure that the project's actionable solutions receive the desired attention and add value to potential users. Moreover, CORE IC will lead Exploitation and Innovation Management, focusing on analyzing, refining, and commercializing Key Exploitable Results (KERs). These efforts will ensure INBLANC's research transitions into market-oriented solutions, fostering innovation and sustainability.
Looking towards a sustainable future
Bringing together 22 partners from 10 countries over the project’s 42-month term, INBLANC will play a significant role in building a more sustainable future. Collecting, organizing, analyzing, and acting on building lifecycle data will help make buildings greener and more efficient.
The consortium partners blend expertise from leading SMEs, renowned universities, RTD institutions, and large-scale industrial organizations, including: DEMO CONSULTANTS , Frederick Research Center, Aalborg University, CORE Innovation Centre, Tampere University, R2M Solution, Z Prime, CYPE, MIWenergía, Colouree, Roelofs & Haase, Comfortica, EPLO European Public Law Organization, Municipality of Thessaloniki, Siemens AG Oesterreich, Zenith, CMB, Estia SA, EPFL, EPIQR Rénovation, Hôpitaux Universitaires de Genève (HUG), ETH Zürich
OPTIMINER Project Launch: A Collaborative Step Towards Sustainable Mining
Author: Eleni Natsi
February 27th 2025
The OPTIMINER project’s kick-off meeting, held over two days in Athens, Greece, marked a significant milestone in the journey to revolutionise Europe’s mining industry. Organised successfully by I-SENSE Group/ICCS, the project coordinator, the meeting served as a key platform for all project partners to gather, discuss the core aspects of the project, and set the stage for what promises to be a transformative European initiative.
During the meeting, each partner had the opportunity to introduce themselves and engage in a fruitful discussion about the project's goals, the different work packages, and upcoming steps. A thorough presentation of the six use cases in Spain, Greece, Poland, Finland, and Chile, focusing on CRM recovery (specifically magnesium, tungsten, REE, especially neodymium, copper, cobalt, and coking coal), took place.
These detailed presentations offered insights into the planned steps, implementation strategies, and anticipated outcomes. The discussions emphasised the importance of applying cutting-edge technologies to real-world scenarios, which will be key to the project’s overall impact.
The event culminated with a delightful dinner in the heart of Athens, providing a relaxed atmosphere for project members to connect, exchange ideas, and reinforce their shared commitment to the project’s success.
The vision
The OPTIMINER project aims to tackle one of the most pressing challenges in Europe’s mining sector: efficiently and sustainably recovering critical raw materials (CRMs) from complex, low-grade ores. This ambitious initiative blends advanced technologies with sustainability efforts, striving to enhance mining efficiency while minimising environmental impact. At its core, OPTIMINER integrates innovative, AI-driven solutions to address challenges across five key modules:
REMINER: Advanced CRM recovery technologies, such as smart ore sorting, bioleaching, and phytomining, powered by an AI-driven CRM Recovery Selector.
DIGIMINER: A digital platform for smart monitoring and control, featuring a Decision Support System, Virtual Miner assistant, and Digital Twins for process optimisation.
ECOMINER: Tools designed to optimise energy and water use, along with waste valorisation, contributing to enhanced sustainability and resilience.
DEMOMINER: Real-world pilot demonstrations in Spain, Greece, Poland, Finland, and Chile, focused on CRMs like magnesium, tungsten, neodymium, copper, cobalt, and coking coal.
GLOBEMINER: Promoting global awareness and fostering EU-Chile strategic cooperation to accelerate market uptake.
CORE Innovation Centre’s Role
As a key partner in the OPTIMINER project, CORE IC will play a pivotal role across several areas:
Technical plan preparation: Leading the effort for the technical plan of each use case, including technical specifications and technological expertise (AS IS situations, data availability, sensors connectivity, and other operating systems).
CRM Recovery Selector: Responsible for defining the criteria and parameters for the CRM Recovery Selector, developing the technology database, and creating detailed profiles for each use case. This also includes the development of customizable algorithms and UI design, along with integrating a digital assistant based on Natural Language Processing (NLP).
Virtual Miner: Tasked with developing an NLP-based multi-role assistant capable of verbal interaction with miners, operators, and managers in the field. This virtual assistant will integrate with the OPTIMINER DSS to provide real-time verbal insights on predictive analytics and strategic planning.
DIGIMINER Platform: Leading the design of the DIGIMINER platform, built on data-driven Digital Twins. This will leverage sensor, historical, and operational data with proper abstraction and distribution among data sources. The goal is to design the connections and interactions of Digital Twins, DSS, and Virtual Miner, as well as establish a cloud infrastructure, featuring a hybrid data warehouse. The platform will also include a modular AI-augmented market observatory to forecast mining market values.
Dissemination, Communication, and Innovation Management: Taking the lead in raising awareness about the project’s outcomes and its impact on sustainable raw materials production. The focus is on communication, exploitation, and innovation management to ensure that the benefits of OPTIMINER reach broader audiences and translate into actionable solutions within the industry.
Stefanos Kokkorikos, Co-Founder & Managing Partner of CORE Group, presenting CORE IC at the kick-off meeting.
Our Consortium
The OPTIMINER project is a collaborative effort, involving 21 partners from 8 countries, combining academic and industrial expertise. With a total budget of €7.29 million over 48 months, the project aims to integrate state-of-the-art technological developments with practical, on-the-ground applications.
Notable partners in the OPTIMINER project include: I-SENSE Group/ICCS, Tapojarvi, JSW (Jastrzębska Spółka Węglowa SA), SALORO SL, Leonore Development, TERNAMAG (part of TERNA S.A.), EUROCORE CONSULTING, AHK Business Centre SA, University of Natural Resources and Life Sciences, Vienna (BOKU), EcoCastulum, CogniSensus, CORE Innovation Centre (CORE IC), ITA · Instituto Tecnológico de Aragón, Fraunhofer Chile, DigitalTwin Technology GmbH, Łukasiewicz – IMN, Główny Instytut Górnictwa (GIG) - Państwowy Instytut Badawczy, Fraunhofer, Iberian Sustainable Mining Cluster | ISMC, LIBRA AI Technologies and HANNUKAINEN MINING OY.
Looking ahead
With the successful launch of the OPTIMINER project, the partners are now focused on the practical implementation of the project’s use cases, the development of new technologies, and increasing awareness through targeted communication and dissemination activities. The coming months will be crucial as teams work to bring the project’s objectives to life.
The OPTIMINER project holds tremendous promise for the future of mining, offering innovative and sustainable methods for recovering and utilising critical raw materials -essential for Europe’s green transition and the global mining industry.
FAIRE: Federated Artificial Intelligence for Remaining useful life Edge analytics
Revolutionising Industrial Operations with FAIRE: Federated AI for Predictive Maintenance
Author: Konstantina Tsioli, Pavlos Stavrou
February 20th 2025
At CORE Innovation Days in January, CORE unveiled a groundbreaking demonstration of FAIRE (Federated Artificial Intelligence for Remaining Useful Life Edge Analytics), a cutting-edge solution that combines AI, edge computing, and federated learning to address critical challenges in industrial operations.
This innovative approach not only enhances operational efficiency but also ensures data privacy and scalability, making it a game-changer for industries like manufacturing, energy, and pharmaceutical.
What is FAIRE
FAIRE is a ground-breaking solution based on the MODUL4R and RE4DY EU projects. FAIRE is a federated AI solution designed to optimise industrial processes by leveraging edge computing and federated learning.
It enables real-time data processing and predictive analytics, while keeping sensitive data secure and on-premise. FAIRE showcased how it can be applied to predictive maintenance for CNC machines, but its applications extend far beyond this use case.
Key FAIRE Features
Edge Computing: This solution utilises edge devices deployed directly on the shop floor to collect and process data locally. This reduces latency, minimises bandwidth usage, and ensures real-time insights without relying on constant cloud connectivity.
In the demo, two edge devices were connected to CNC machines, collecting data relevant to tool wear and predicting the Remaining Useful Life (RUL) of milling tools.
Remaining Useful Life (RUL): is a predictive tool that estimates the time left before a machine or component fails or requires maintenance, based on real-time data and historical performance patterns. In the context of FAIRE, the RUL model predicts tool wear in CNC machines, enabling proactive maintenance and reducing downtime while ensuring data privacy and security.
Federated Learning: FAIRE employs federated learning to enable collaborative intelligence across multiple machines or factories. Instead of sharing raw data, only model parameters (e.g., insights and updates) are sent to a central server, ensuring data privacy and compliance with regulations like GDPR. This approach allows machines to "learn" from each other, improving prediction accuracy and operational efficiency without compromising sensitive information.
Data Privacy and Security: By keeping data on-premise and sharing only model updates, FAIRE ensures that proprietary information remains secure. This is particularly important for industries with strict data protection requirements.
Scalability and Flexibility: FAIRE’s architecture is designed to scale effortlessly. As new machines or edge devices are added to the network, they can seamlessly integrate into the federated learning ecosystem, enhancing the system’s overall intelligence and resilience.
Predictive Maintenance for CNC Machines
The demonstration of FAIRE solution focuses on a real life application: predictive maintenance for CNC machines. Here’s how it worked:
Data Collection: Two edge devices were connected to two CNC machines, collecting real-time data on tool wear and machine performance using industrial protocols like OPC-UA and MQTT.
Local Processing: The edge devices preprocessed the data locally, running AI models to detect anomalies and predict RUL. Results were displayed on monitors, providing operators with actionable insights.
Federated Learning: Model updates from each edge device were aggregated to a central server to update the global model. The updated model was then sent back to the edge devices, enhancing their predictive accuracy.
Real-Time Insights: Operators then could monitor tool wear and RUL in real time, enabling proactive maintenance and reducing downtime.
The benefits of FAIRE
FAIRE offers numerous benefits for industrial operations:
Smarter Machines: Continuous learning and adaptation improve machine performance and operational efficiency.
Enhanced Data Privacy: Sensitive data remains on-premise, ensuring compliance with data protection regulations and/or requirements.
Cost Optimisation: Reduced data transmission and proactive maintenance minimise operational costs.
Collaborative Intelligence: Federated learning enables machines to learn from each other, improving model accuracy across the network.
Scalability: The solution can easily scale to include additional machines or factories, making it suitable for large industrial networks.
Application areas
While the demonstration of FAIRE solution involved an example of CNC machines, its capabilities extend to various industries:
Pharmaceutical: In a sector where protecting sensitive and production data is paramount, this solution safeguards data privacy and security.
Automotive: Enhance predictive maintenance for automotive production lines.
Aerospace: Improve the performance and reliability of aircraft components.
Energy and Smart Grids: Monitor and optimise power grid equipment like transformers and substations.
Mining: Optimise the operation of heavy machinery like excavators and drilling equipment.
FAIRE represents a significant leap forward in industrial AI, combining the power of edge computing and federated learning to deliver real-time insights, enhance data privacy, and optimise operations. By addressing critical challenges like unexpected downtime, inefficient data handling, and legacy equipment limitations, FAIRE empowers industries to achieve smarter, safer, and more efficient operations.
Solutions like FAIRE will play a critical role in shaping the future of industrial automation and data-driven decision-making.
The StreamSTEP Horizon Europe project officially Kicks off
Author: Maria Lentoudi
November 27th 2024
The official kick-off meeting of the StreamSTEP project took place on November 13–14, 2024, in Trondheim, Norway. The event was hosted by the Project Coordinator, SINTEF, and brought together representatives from the 30 partner organizations that comprise the StreamSTEP Consortium. The meeting provided a platform for partners to introduce their institutions and outline their specific roles in this pioneering research initiative.
During the two-day meeting, each partner had the opportunity to present their expertise and engage in productive discussions on the project’s objectives, work packages, and forthcoming activities. These presentations offered valuable insights into planned methodologies, implementation strategies, and expected outcomes. A recurring theme throughout the discussions was the critical importance of applying cutting-edge technologies to real-world industrial challenges—an approach that is central to achieving the project’s goals and maximizing its impact.
The StreamSTEP project
Over the course of its four-year duration, the StreamSTEP project aims to improve heating energy management in industrial processes. The project will deploy five innovative heat exchanger prototypes for waste heat across temperatures from 135°C to over 1400°C. High-temperature heat pumps will enhance heat recovery, achieving outlet temperatures of 150°C and 215°C, with improved performance through ejector technology. Advanced manufacturing techniques and novel material alloys will enable these innovations. The system will be demonstrated across five sectors, copper, ceramics, silicon, plastics and oil & gas, with significant impacts on waste heat recovery, productivity, and energy flexibility.
StreamSTEP will adopt advanced solutions, comprising both technologies and practices, for sustainable heating energy in Energy Intensive Industries. These solutions are expected to have a positive impact on process industries, meaning:
Consolidating a process data collection and management platform, providing the digital infrastructure needed for the planning and management of process lifecycle.
Consolidating a process hybrid Digital Twin (HDT) framework to enhance the design and management of heat recovery and reuse processes.
Reduce the environmental footprint of heat exchanger and heat pipe components in manufacturing.
Deliver 5 advanced Heat Exchanger prototypes – DC1 Copper, DC2 Ceramic, DC3 Silicon, DC4 Plastic, DC5 Oil and gas.
Comprise 5 demonstrators, complemented by an impact booster, amplifying the outcomes in terms of marketability, social value creation and accelerate StreamSTEP adoption.
CORE Innovation Centre’s Role
As a key partner in the StreamSTEP project, CORE IC will play a pivotal role across several areas:
Data architecture design and orchestration: Our team will lead the development of the project's digital platform as well as the integration of the project’s technical components into the platform — including data architecture, middleware for the digital infrastructure, smart IoT integration
Predictive maintenance function integration: Based on our well-established know-how through our the COREbeat platform, our team will develop Predictive Maintenance solutions for Heat Exchangers and Heat Pumps
Process digital twin and HMI integration: Our role will be to coordinate the integration of hybrid process digital twins for each of the processes selected.
Energy Management System (EMS): CORE IC will expand the Energy Management System (EMS) both for offline planning and real-time operation using advanced techniques such as machine learning and deep neural networks to optimise industrial processes and energy efficiency.
Dissemination, Communication, and Innovation Management: CORE IC will also lead the communication and dissemination activities, increasing project awareness and build StreamSTEP’s reputation across crucial target groups. Moreover, CORE IC will lead the Exploitation Strategy and IPR Management, focusing on analysing, refining, and commercialising Key Exploitable Results (KERs).
The StreamSTEP consortium
StreamSTEP is a collaborative effort involving 30 partners from 9 countries across the European Union, as well as Norway, the United Kingdom, and Switzerland. The consortium includes a diverse group of participants—universities, research institutes, SMEs, and key industry stakeholders such as suppliers and end-users—working together on this ambitious and transformative initiative.
With a total budget of €14.99 million over a 48-month period, StreamSTEP aims to revolutionize the management of thermal energy in industrial processes, enhancing efficiency and sustainability across sectors.
The project brings together a multidisciplinary team of experts from across the innovation landscape, including: SINTEF, SINTEF Energy, CORE Innovation Centre, ΑΙΜΕΝ Technology Centre, BRUNEL University London, Instituto Tecnológico de Aragón, Imperial College London, Institute of Computer and Communication Systems, CIRCE Centro Tecnologico, Vytautas Magnus University, Technical University of Crete, Tampere University, University of Seville, Institute for Ceramic Technology, ALEA Design, MIWenergía, Econotherm, Expander Tech, Teknopar, Enviva, INCOTEC, Gruppo FOS, GEP, HALCOR, Torrecid, ELKEM, Elbi, Repsol, Future Materials, Proval SARL.
You can find more information on the StreamSTEP project website, designed by our team.
CORE Group participated in a focus group workshop at HALCOR
Author: Maria Tassi
November 13th 2024
A successful Focus Group Workshop was held on 31st of October 2024 at the HALCOR facilities, in Boeotia, Greece as part of the CARDIMED project.
Participants in the workshop included representatives from CORE Group, ICCS, NTUA, HALCOR employees and managers, as well as regional stakeholders, enabling collaboration and knowledge exchange.
The CARDIMED project
CARDIMED is a project funded by the Horizon Europe Programme focused on boosting Mediterranean climate resilience through widespread adoption of Nature-based Solutions (NBS) across regions and communities.
Our CORE team will develop a cloud-based orchestration middleware for efficient data handling across diverse sources, and also focus our efforts on industrial symbiosis through smart water management in the HALCOR demo site, using digital twin technologies.
The workshop aimed at promoting innovative solutions in industrial manufacturing, conducted in the context of Digital Solutions creation that offer tailored views for visualising information to non-experts, citizens etc., with emphasis on the Demonstration case of Industrial symbiosis through smart water management.
Workshop goals and objectives
The main objective of the workshop was to engage end users to gather feedback and prioritise the requirements, and consequently translate the business requirements of end user, HALCOR, to technical requirements leading to implementation of digital solutions and bringing innovation to the industry.
The workshop was opened by M.Sc. Efstathia Ziata (HALCOR), who presented the CARDIMED project and its objectives. Following her presentation, Dr. Ioannis Meintanis (CORE IC) gave insights on the digital twin solution, which is a replica of a physical asset that simulates its behaviour in a virtual environment, highlighting its role in supporting Water-Industrial Symbiosis within HALCOR's factories.
Dr. Maria Tassi (CORE IC) presented other Digital Solutions implemented as part of the HALCOR demo, such as the Nature-Based Solutions (NBS) definition and scenario-based impact assessment interface, the climate resilience dashboards and data storytelling, the citizen engagement app and intervention content management and the NBS exploitation and transferability support module, highlighting their potential to enhance efficiency and sustainability in operations.
Notable contributors to the round table discussions included M.Sc. Katerina Karagiannopoulou (ICCS) and M.Sc. Nikolaos Gevrekis (CORE IC), who provided valuable perspectives on the digital solutions.
Impact on Industry
The success of the workshop lies in end users’ discussions on the various digital solutions, who provided valuable feedback and prioritised user requirements to be integrated in the Digital Twin solution. Their insights will be critical in shaping a final product that effectively addresses the evolving demands of the industry.
These innovations are set to significantly impact the manufacturing industry, by enhancing resource efficiency and sustainability. They will help optimise water usage and promote resource reuse across interconnected processes, leading to cost savings and reduced environmental footprints.
CORE Group’s collaboration with HALCOR
These technological advances will enable HALCOR to optimise its manufacturing processes and resource management in real time, resulting in improved operational efficiency, significant cost savings and reduced water consumption. By adopting sustainable practices, HALCOR can strengthen its reputation as an industry leader in sustainability and appeal to environmentally conscious stakeholders.
HALCOR is a strategic partner for CORE Group, with a collaboration extending across three more Horizon Europe projects, TRINEFLEX, StreamSTEP and THESEUS. As part of the TRINEFLEX project, HALCOR has integrated COREbeat, CORE Group’s all-in-one Predictive Maintenance Platform at its Copper Tubes Plant facility in Boeotia. COREbeat’s asset monitoring capabilities are helping HALCOR acquire deep monitoring insights and increase the availability, flexibility, efficiency and reliability of their equipment.
COREbeat, our all-in-one Predictive Maintenance solution, relies on the beatBox hardware component, pictured here.
The TEAMING.AI project reaches a successful conclusion
Authors: Maria Lentoudi, Ioannis Batas
26th September 2024
The TEAMING.AI project has officially wrapped up its activities, with a Final Review meeting held earlier this summer in Valencia, at the premises of Industrias Alegre. This meeting marked the culmination of 3.5 years of dedicated effort, showcasing the remarkable outcomes of this collaborative project.
Comprising a consortium of 15 partners from 8 countries, TEAMING.AI entered into force in January 2021 with the goal of increasing the sustainability of EU production with the help of Artificial Intelligence. The project has since yielded remarkable results, including more than 23 open-access publications.
Project overview
TEAMING.AI project’s aim was to make breakthroughs in smart manufacturing by introducing greater customisation and personalisation of products and services in AI technologies. Through a new human and AI teaming framework, the aim of our consortium was to optimise manufacturing processes, maximising the strengths of both the human and AI elements, while maintaining and re-examining safety and ethical compliance guidelines.
This was achieved through the development of an innovative operational framework, designed to cope with the heterogeneity of data types and the uncertainty and dynamic changes in the context of human-AI interaction with update dynamics more instantly than with pre-existing technologies.
Our CORE team led on the Dissemination and Exploitation Work Package, being involved in various tasks within the project framework to expand TEAMING.AI’s impact. More specifically, we led the project’s strategic management and replicability, as well as leading the dissemination and communication strategy.
Strategic Management & Replicability of TEAMING.AI
The CORE innovation team was responsible for the strategic management of the consortium, identifying Key Exploitable Results of the project and carrying out market analysis. Our work for this part of the project included:
Identifying Market Barriers: Our team conducted a market barriers analysis, based on input provided through a custom questionnaire. The project’s end users were surveyed, and the survey was also circulated to the ICT-38 2020 projects, increasing our end user sample. After completing the survey, we identified mitigation strategies for the barriers discussed.
Pains & Gains: We identified the most significant pains our end users face based on a unique research plan. The results of this part of our research were highly impactful, being included in Chapter 23 of the “Artificial Intelligence in Manufacturing” open access book. You can find out more here.
Value Propositions: Our team identified the value propositions offered throughout the project, through interactive workshops with our partners to help us align the identified jobs, pains, and gains with the Teaming.AI Engine result.
PESTLE Analysis: A PESTLE analysis was performed to describe Political, Economic, Social, Technological, Legal and Environmental factors that are related to Teaming.AI. Results show strong political presence to enable further scale-up activities of the project’s results. The uncertain economic conditions may influence investment decisions. The social factors indicate the need for more efficient activities and upskilling. The technologies are emerging and considered enablers according to Gartner. Finally, from an environmental point of view, results have remained a little stagnant according to the IPCC.
Market Replication & Analysis: As a final task, our team worked on Market Replication. The technology providers relevant to Teaming.AI were considered a possible segment for replication besides the project’s end users. A workshop was held with the project’s technology providers to determine requirements to address these segments.
Dissemination and Communication Activities
When it comes to dissemination and communication, the evaluation revealed a strongly positive outcome for our team’s strategy. CORE worked hand-in-hand with the entire consortium to maximize the project's impact and ensure the project’s objectives were communicated effectively to relevant audiences and stakeholders.
The project’s official website acted as its main communication hub, supported by a strong presence on social media platforms, the creation of various communication materials, including 11 videos throughout the duration of the project, the publication of 33 media articles, the release of 10 dedicated newsletter editions, and 11 press releases. These efforts were aimed at increasing the project’s visibility and public engagement.
TEAMING.AI consortium also produced 28 scientific peer-reviewed publications in top-ranked journals or conferences, attended 43 events delivering 38 presentations, promoted TEAMING.AI through 3 exhibit booths at key industry events, and organised one final conference. TEAMING.AI also joined the AI4MANUFACTURING Cluster and participated in 5 cluster workshops alongside 13 other H2020 and Horizon EU research projects to expand its impact.
A recording of the final workshop is available here for viewing on YouTube. Additionally, 10 more short videos introducing the TEAMING.AI concept and summarizing its research activities are accessible via the project’s YouTube channel.
The project’s website, designed and maintained by CORE, will continue as a central hub for useful information and resources. Visitors can learn more about important research activities performed and results through press-releases, newsletters, open-access scientific papers and public deliverables that can be found on the website.
The project has also shaped significant online communities, with more than 1.300 followers on LinkedIn and 900 on X.
The dissemination and communication activities have played a crucial role in ensuring that the TEAMING.AI project’s activities and results were effectively shared with both scientific and industry communities, as well as the general public.
It was great working with our TEAMING.AI consortium to deliver impactful change in human-AI interactions for the manufacturing sector. Looking forward to future collaborations.
CORE Group awarded 9 new Horizon EU projects
Author: CORE Innovation Centre
10th September 2024
CORE Group and CORE Innovation Centre are joining forces with partners across the EU in a total of 9 new Horizon Europe projects. Our team is responsible for 4 of these successful research proposals.
The new projects bring our total to over 50 research projects, ongoing or completed, and increase our research budget to over 24 million €. We are very excited for these brand new R&I initiatives, a preview of which you can get below, and look forward to working with our partners to transform industry with the power of AI.
DEXPLORE
DEXPLORE seeks to revolutionise mineral exploration in Europe by developing innovative approaches to counter declining ore deposit discovery rates, focusing on deep-seated deposits critical for the economy's decarbonisation. The project emphasises engaging the general public and stakeholders, combining innovation at various levels, and utilising cutting-edge tools and technologies. Targeting essential materials across extensive geological terrains, DEXPLORE proposes a holistic innovation package, integrating UAV-assisted in-field mineral detection, advanced Earth Observation methods, and novel deep-land geophysics techniques to reach at least 600 m depth.
The initiative outlines the development of updated ore models, improving exploration technologies, and providing a visualisation platform. This platform will integrate geological, remote sensing, and geophysical data to enhance access to information about EU potential in critical raw materials, while increasing public awareness. DEXPLORE also aims to strengthen cooperation with strategic partner countries, establishing a robust Advisory Board and fostering collaboration with other EU initiatives for joint activities and result sharing.
DEXPLORE represents a collaborative effort to advance mineral exploration, contribute to sustainable sourcing of critical raw materials, and ensure EU's open strategic autonomy.
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We will determine technical specifications for the software platform and a cloud-based infrastructure, ensuring compatibility with diverse data types and sources. Essential aspects like data ingestion protocols, storage solutions and security measures will be outlined. We will design and implement a robust and secure infrastructure for on-demand data distribution, utilizing established industry tools and frameworks. DEXPLORE will transform data from various devices and components into suitable formats and transmitting them to real-time and near real-time pipelines, through middleware.
We are also in charge of the visualisation dashboard and the UI, catering diverse user needs and preferences and making the user's interaction easier with complex data. We are aiming for an intuitive, responsive, and visually appealing UI with advanced visualization techniques, like 3D accelerated maps by Xcalibur, graphs and charts for data exploration and analysis, while ensuring robust and safe data exchange along. Design, development and use of an Augmented Reality (AR) application by ICCS, will be implemented to the platform, improving awareness of the general public and educate them through immersive visualizations. AR tool will be part of the open days to ensure social participation and engagement.
Additionally, we are heavily involved on the exploitation planning and technoeconomic analysis.
PRIM-ROCK
PRIM-ROCK addresses advanced techniques for the pre-processing of the raw material, calcination and roasting processes, that are commonly used in the mineral and cement industries, supplemented by simulations and decision support systems. The project aims to design, develop, and validate innovative and higher resource efficient processes, optimising existing ones and lowering the level of GHG emissions of extractive industries. AI data-driven models will be utilised and a digital twin for each process will be developed. Finally, the consortium will investigate waste reduction and re-utilisation strategies. The PRIM-ROCK solutions will be demonstrated in 3 different ASPIRE sectors, namely Minerals (magnesite, laterite), Cement (limestone) and Non-ferrous metals (sphalerite, chalcopyrite).
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We are leading the effort of optimization and integration of Digital Twinning processing (DSS & UI), enhancing the resource efficiency of the calcination and roasting processes. The DSS will integrate diverse streams of heterogeneous information from various models (data-driven models, physics-based models or hybrid models). This system will deliver real-time insights and alerts. A prototype of the UI will be designed, tested and refined based on user feedback. The final UI will be developed integrating the data models and the DSS and user acceptance testing will be conducted.
CORE IC is also leading the effort of Virtual scale up and expansion study suggesting configurations within the energy system’s boundary conditions, linking of additional added value technologies, mainly CCUS and synthetic fuel production. A Reinforcement Learning (RL) algorithm will be employed to train the planning model as well as Black-box models
Finally, we are leading the 3-phased dissemination and communication activities. The early phase will be dedicated to raising awareness among the target groups and will include the brand and visual identity of PRIM-ROCK, with Dissemination activities targetingboth the academic and industry professional communities, through online events like webinars, congresses, etc. The middle phase will be dedicated to growing and consolidating awareness, informing stakeholders and groups about technological breakthroughs and business benefits. The final phase will ensure the long-term impact within targeted communities.
ALCHEMHY
ALCHEMHY aims to develop a set of innovative electrified processes to produce platform chemicals, particularly ammonia, and methanol and a Plasma-Catalytic Hydrogenation process (PCH). To this end, processes configuration will be optimised and novel materials and catalysts compatible with the electric input will be developed, optimising their performance, ramp-up times and enabling milder conditions. This will support the intensification and downscaling of the ammonia and methanol production, facilitating decentralised production integrated with downstream processes and renewable energy generation. The project will contribute to the development of a sustainable chemical industry, by decarbonising the production of both chemicals, supporting the creation of green jobs and improving the competitiveness of European industries, while contributing to a more resilient and secure energy system for the EU, reducing its dependence on imported fossil fuels.
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CORE Group is leading the development of AI-based data-driven models of the methanol and and ammonia production processes and their combination with the ROMs produced, in order to develop the Hybrid Digital Twin of each of these projects. Data-driven models will consume the available data from the lab scale facilities. Moreover, exploitation of the generated data will provide further insights and information that can attempt to correlate any process parameters with the relevant molecular dynamics properties and interactions of the involved products, optimising the manufacturing parameters and detecting any potential anomalous behaviour of the involved assets. The investigation of Physics-Informed models can be performed for validating the performance of the generated ROMs.
Additionaly, we are responsible for ALCHEMY's Exploitation Plan and IPR management, which involves a three-phase process of analysing results, defining exploitation routes, and developing a post-project roadmap to ensure effective commercialisation and industry impact. The plan includes tailored business models, market size and competition analysis, and a SWOT analysis to address customer needs and validate the value proposition of ALCHEMY's KERs
StreamSTEP
StreamSTEP is a collaborative initiative by 31 organisations across the EU, Switzerland, and the UK, aimed at enhancing heating energy management in industrial processes. The project focuses on waste heat recovery across a wide temperature range using innovative heat exchanger prototypes and high-temperature heat pumps. Advanced manufacturing techniques and novel material alloys will enable these innovations, which will be demonstrated in five sectors: non-ferrous metals, ceramics, minerals, plastics, and refining. Integral to the project is a holistic process digital twinning pipeline, providing infrastructure for optimization agents to manage energy balance, storage, GHG avoidance, and data-driven LCA, ultimately recovering and reusing 50%-90% of waste heat with a payback period of less than three years, while boosting productivity and energy flexibility.
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CORE IC is responsible for the development and integration of the project's technical components. This includes creating the data architecture and middleware for the digital infrastructure, integrating smart IoT components, and developing hybrid process digital twins. Additionally, CORE leads the effort to expand the Energy Management System (EMS), implementing advanced techniques like machine learning and deep neural networks to optimise industrial processes and energy management .
CORE IC will also be handling the project's communication and leading on the exploitation strategy, with Exploitation Routes (technical and commercial) for KERs, considering IPRs, ensuring that project results are effectively communicated and positioned for market uptake, maximising the impact and commercial potential of the innovations developed.
OPTIMINER
The OPTIMINER project addresses Europe's challenge of efficiently and sustainably recovering Critical Raw Materials (CRMs) from complex and low-grade ores. It aims to reduce Europe's heavy reliance on CRM imports by innovating in recovery methods and promoting sustainable mining practices. Key components include advanced technologies like an AI-enabled CRM Recovery Selector and a digital platform (DIGIMINER) for smart monitoring and control. ECOMINER focuses on sustainability through energy and water optimization, waste valorisation, and toxicity management. DEMOMINER showcases pilot lines across multiple countries, demonstrating practical applications, while GLOBEMINER promotes market awareness and strategic EU-Chile cooperation in CRM recovery.
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CORE IC is leading the effort for the technical plan of each use case, including technical specifications and technological experties (AS IS situations, data availability, sensors connectivity, other operating systems). We are also in charge of the CRM Recovery Selector, defining criteria and parameters, developing technology database with a detailed profile of each use case, along with algorithm development with customizable capabilities and a UI design intergating a digital assistant (NPL-based).
The DIGIMINER platform will be built on data-driven Digital Twins, by leveraging sensorial, historical and operational data and proper abstraction and distribution among the data sources. We will design the connections and interactions of Digital Twins, DSS and Virtual Miner, as well as, establish a cloud infrasturucture, featuring hybrid data warehouse. New data from simulation results and experimental data cross corellation will be adjusted to the model. A modular AI-augmented market observatory will be designed forecasting mining market values.
The team of experts at CORE IC will also be leading dissemination and communication, as well as exploitation and innovation management activities, with a heavy focus on awareness about the project's outcomes and their impact, emphasizing sustainable raw materials production.
Theseus
The Theseus project focuses on implementing Industrial-Urban symbiosis (I-US) through Hubs4Circularity (H4C) in Europe, starting with the Athens/Attica region in Greece. This initiative involves municipalities and industries collaborating to manage resources, waste, energy, water, and infrastructure in a sustainable manner. The project aims to establish the first-of-its-kind H4C hub in Greece, leveraging regional needs and digital technologies to develop solutions for water, energy, and materials. These efforts align with EU objectives, aiming for climate neutrality by 2050 and closing resource loops through innovative governance models and stakeholder cooperation. Theseus integrates existing innovations and aims to replicate successful solutions across other EU regions, drawing parallels to the transformative legacy of Theseus in Athenian mythology.
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CORE Group will work on defining key performance indicators (KPIs) for monitoring impacts across various dimensions, facilitating informed decision-making in subsequent project tasks. The project focuses on coordinating digital plans for pilots and mapping data requirements to finalize a comprehensive data model. CORE Group will also be leading the integration of diverse data sources and ensuring secure and efficient data exchange across all project activities, collaborating closely with partners to implement cost-effective and robust data acquisition measures. Additionally, we are going to design and operate a Predictive Resource Logistics Module (PRLM) within the Hubs4Circularity framework, predicting material and water flows and generating socio-economic and environmental indicators. It aims to identify bottlenecks, business opportunities, and scalability options, with plans for post-project automation. A digital platform will be developed, integrating various modules and services for industrial symbiosis, enabling collaboration and resource tracking, supported by advanced analytics and user feedback for iterative platform optimization. Outputs include detailed reports and functional platform releases.
JOULIA
The JOULIA project aims to develop and demonstrate innovative induction and microwave heating processes for rubber vulcanisation and glue thermal activation in the rubber and plastic sectors, optimising them for flexibility, energy savings, and integration of renewable energy sources. These processes will be refined using digital models and simulations, ensuring adaptability, cost optimization, and compliance with health and safety standards, while predictive maintenance tools support ongoing operation. The project involves 16 partners from 7 EU countries and aims to enhance European industrial resilience, decrease fossil fuel dependence, and improve energy efficiency and sustainability, with potential applications in other sectors like food and ceramics.
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We are responsible for leading the identification of Funding Sources and Financial Instruments that can support the replication and upscaling of JOULIA innovations. We are also involved in the task of business models development that leverage the project's technological advancements to identify new market opportunities and in the task of the identification of Regulatory and Standardization Barriers that may hinder in the market uptake of JOULIA technologies.
We are involved in exploitation management tasks, contributing to the overall coordination and decision-making processes within the project.
rEUman
The European remanufacturing industry is essential for Europe's sustainable transition due to its energy, material, and functionality savings, along with significant socio-economic benefits like job creation and technological advancement. To enhance competitiveness and future-proof the industry, it is crucial to address barriers such as limited automation, poor human inclusion, and lack of digitalisation.
The rEUman project aims to develop a human-centric remanufacturing approach by improving factory-level regeneration and traceability and ensuring stability in the value-chain, while demonstrating its effectiveness in the automotive, home appliances, and optoelectronics sectors.
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Our technical role in the rEUman project encompasses the design and development of digital and AI-driven systems that evaluate the condition of returned products through image analysis at collection points, such as workshops or service centers. By assessing the remanufacturability of parts, the system aims to optimize decision-making regarding whether parts should be sent for remanufacturing, considering both technical and economic feasibility. This process is intended to minimize logistics costs and environmental impacts by avoiding the unnecessary transportation of non-remanufacturable parts.
INBLANC
Significant challenges in the building and construction value chain stem from fragmentation and siloing, necessitating a systemic change through lifecycle perspectives to uncover interactions and opportunities. INBLANC aims to establish an open ecosystem focused on building lifecycle data, using low-cost data collection, consolidation in Building Digital Logbooks, and interfacing with EU dataspaces. The project will demonstrate its approach through six diverse demo cases, engage actors across the value chain, and integrate high-value services for energy planning, facility management, and renovation planning, aiming for near-market readiness.
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CORE IC is responsible for the development of smart energy services tools like the RES Selector and Energy Management System (EMS), which optimise renewable energy source investments and balance system energy consumption and their validation using gap detection and optimization for city-scale identification and remedy recommendation. We are creating new technologies using high-performance computing infrastructure for tasks such as deep learning model development and energy consumption forecasting that support support advanced modeling, simulation, and machine learning applications.
Furthermore, CORE is also leading the communication, dissemination, innovation and exploitation activities.
We are also leading the communication, dissemination, innovation and exploitation activities.
The InComEss project wraps up
Authors: Clio Drimala, Dimitris Eleftheriou
19th March 2024
Having successfully completed 4 years of operations, the InComEss project officially wrapped up its activities last month, and held the project’s Final Review Meeting with the European Commission’s Project Officer on March 13, in Brussels, at the premises of SONACA.
With a core team of 18 partners from 10 countries, InComEss entered into force in March 2020. Now, after a four-year lifespan, the project has yielded remarkable results, including more than 17 open-access academic publications, and has driven outstanding research on the development of polymer-based smart materials with energy harvesting and storage capabilities in a cost-efficient manner for the widespread implementation of the Internet of Things (IoT).
CORE Group was involved in various tasks within the project framework to expand InComEss’s impact. In particular, we were responsible for devising and managing the consortium’s exploitation strategy, as well as leading the dissemination and communication strategy.
Project overview
Besides our involvement, overall achievements of the project include the development of:
Piezoelectric and thermoelectric energy harvesters with a proven ability to generate electricity through mechanical vibrations and temperature differences.
Monolithic printed supercapacitors that demonstrated their efficacy to store the harvested energy when integrated with a conditioner circuit and generators.
A power conditioning circuit that enhances energy transfer efficiency between generators and end-use electronics.
A miniaturized Fibre Optic Sensors (FOS) interrogator, with reduced power consumption, was showcased for its utility in energy harvesting.
Furthermore, Bluetooth Wireless MEMS and FOS communications were optimized and seamlessly integrated into an IoT platform, offering data monitoring capabilities. Among the research highlights being implemented within InComEss are also three impactful use-cases within the aeronautic, automotive, and smart buildings sectors.
Exploitation activities
The exploitation activities encompass an exhaustive market analysis targeting the consortium’s end users and other markets that could potential leverage the project’s innovations. The specific markets addressed were: 1) Smart Buildings, 2) Aeronautics, 3) Automotive, 4) Oil & Gas Pipelines, 5) Sports Environment, 6) Pacemakers, 7) Railway, and 8) GPS tracking devices. We identified market barriers that would slow down the adoption of the project’s technologies, which we categorized in regard to their nature (Sociopolitical, Economic, Environmental, Technological, Organisational). Based on the information provided, unique selling points of the results with commercial orientation were discerned.
Moreover, results were identified with a clear IPR protection path and exploitation route option. Partners decided whether they would use their results for further research or commercially. We developed business models for the more marketable results based on sustainability-oriented archetypes. The business model included the list of partners participating in the commercial exploitation and their associated activities and resources required to bring the system to the abovementioned market segments. Potential avenues such as ΣEureka and InnoEnergy were considered to reduce the initial investment costs and improve access to market.
The activities were manifested in the development of business plans for the Automotive and Aeronautics use cases. The analysis considered the potential benefits that the route-to-market partners would receive, namely Photonfirst and Smart Material and specifically the point where they would expect a return on their initial investment if they further progressed their results. Based on the activities and resources needed, an appropriate revenue model was in place to perform a financial analysis for both use cases. Moreover, we worked on a cost-benefit analysis for the end users to understand their benefit of acquiring the commercialized version of the InComEss system. Specifically, the aeronautics scenario included an installation in the wing slats, while the automotive scenario in the exhaust systems.
Dissemination and Communication Activities
When it comes to dissemination and communication (D&C), CORE devised and oversaw the dissemination and communication strategy, working hand-in-hand with the entire consortium to maximize the project's impact and resonance.
The InComEss team generated 17 open-access scientific articles, an important legacy of the project, and plans to publish 7 more in the upcoming months. Partners also participated in 32 events delivering 50 presentations and a lecture, presenting 5 posters and promoting InComEss through 2 exhibit booths and a stand in landmark industry-related events.
Beyond that, 2 workshops were organized namely, Mid-Term Workshop on InComEss EU Project and the InComEss Final Workshop. Video recordings from the workshops are available to watch here and on YouTube [Part 1], [Part 2]. 11 more short, engaging videos introducing the InComEss concept and recapping its research activities are also available on the project’s YouTube Channel.
The project’s website, designed and maintained by CORE, will continue as a central hub for useful information and resources. Visitors can learn more about important research activities performed and results through 11 press-releases, 10 newsletter issues, open-access scientific papers, public deliverables and training materials that can be found on the website.
The project has also shaped significant online communities, with more than 1000 followers on LinkedIn and 700 on X, another reflection of the overall effectiveness of the InComEss D&C strategy.
It has been a pleasure working with all our partners for the InComEss project.
Until the next one.
The MOSES project reaches a highly succesful conclusion
Authors: Pantelis Papachristou, Konstantinos Nikolopoulos
13th March 2024
After 3 and a half years, the MOSES project has reached its culmination, marked by a closing conference held online. The central goal of the project was to enhance the Short Sea Shipping (SSS) component of the European container supply chain by implementing the following three groundbreaking innovations:
The development of a hybrid electric feeder vessel, equipped with a robotic container-handling system, to increase the utilisation rate of small ports.
The establishment of a digital collaboration and matchmaking platform to match demand and supply of cargo volumes, utilising Machine Learning (ML) to maximise Short Sea Shipping traffic.
The introduction of an autonomous vessel maneuvering and docking scheme, based on the cooperation of a swarm of autonomous tugboats coupled with an automated docking system.
Our role in the project
As part of the project, CORE has been involved in the third innovation concept, pioneering the transition from traditional docking procedures to an autonomous swarm of tugboats. These advancements were facilitated by creating a sophisticated simulation environment and the application of ML techniques, which refined docking strategies. This digital twin technology, coupled with an AutoPilot control system, exemplifies a significant leap forward in maritime operations, reducing docking time and enhancing port service availability and environmental sustainability.
Machine learning approach
Initially, our team created a virtual environment to simulate the real-life components, such as the port, water mass, tugboats and containership. To ensure fidelity to actual conditions, the virtual environment integrated results from hydrodynamic simulations conducted by MOSES partners, analysing the navigation and evaluating the hydrodynamic parameters, such as the friction resistances for each ship object separately. Additionally, Finite Element Model simulations (FEM) were employed to assess the interactions between the tugboats and the containership, by evaluating force-reactions and stresses.
MOSES Unity test scene displayed during training of 3 push agents next to the “Advanced Ship Controller” and “Behaviour parameters” component.
The simulation environment served as a training environment for the developed swarm intelligence machine learning algorithm, allowing agents to learn from their experiences. Specifically, the agents (tugboats) were trained using deep reinforcement learning techniques, where the learning procedure is based on the interaction of the agents with the environment and the accumulation of feedback (rewards or penalties), while the agents collected observations through LiDAR and GPS sensors. The goal was to discover optimal strategies that maximise cumulative rewards over time. The developed digital twin was deployed at the edge, along with an AutoPilot system to control the steering and thrust of the tugboats based on the digital twin’s inference.
The digital twin was successfully demonstrated and validated in relevant environment (TRL6) at the Faaborg port in Denmark, employing a swarm of two tugboats pushing a bargue towards the dock. The accompanying video below illustrates the precision of the simulation outcomes (displayed on the left-hand side) compared with the actual real-world demonstration (on the right-hand side). This live demonstration underscored a remarkable achievement: more than a 25% reduction in manoeuvring and docking times, leading to a corresponding significant decrease in port emissions and a notable increase in the availability of port services.
Comparison of the simulation outcomoes (left-hand side) with the real demonstration in Faaborg port (right-hand side) considering the scenario where two tugboats push a bargue to the dock.
The commercilisation phase
To ensure successful commercialisation, CORE developed an Innovation Strategy, focusing on clear value propositions and competition mapping. Additionally, CORE developed a model for profit simulation, with a focus on the innovations introduced by the autonomous tugboat system, which is the only technology solution combining autonomous operation, sustainability and safety, with the highest TRL and exposed in real conditions.
Understanding our pilot
For more information on Pilot 1 of the MOSES Project, where our technical team was heavily involved, our consortium partners have created a comprehensive video explaining the AutoDock System and how it works. You can watch it below.
Over the past 42 months, we were very happy to work closely with our consortium partners to successfully deliver an autonomous vessel manoeuvring and docking system which has the potential to completely transform the Short Sea Shipping and container supply chain of the European Union.
We look forward to future, even more fruitful collaborations.
CORE Group takes on 7 new EU research projects
Author: CORE Innovation Centre
20th November 2023
CORE Group and CORE Innovation Centre are continuing on our trailblazing EU research journey, participating in a total of 7 new Horizon Europe projects. Our new research projects, which focus on the digital and green transitions, will help us put our expertise to good use, always focusing on our four CORE missions; Digital Transformation, Digital Tools, Digital Twins and Green Industry.
The new projects bring our total to over 40 research projects, ongoing or completed, and increase our research budget to over 13 million €. We are very excited for our participation in all these new projects, a preview of which you can get below, and look forward to working with our partners to deliver impactful change through machine learning.
M4ESTRO
M4estro aims to revolutionise manufacturing with its trust-based Manufacturing as a Service (MaaS) platform, offering proactive resilience and disruption readiness. It aims to unite industrial stakeholders for secure service exchange and emphasize workforce development. The platform analyses internal and external factors, aiming for reduced process ramp-up times, enhanced efficiency, lower costs, and job creation, ultimately delivering a significant return on investment for the consortium. M4estro, beyond a research project, can serve as a transformative force for industry resilience and sustainability.
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As part of the project, our team will create an optimisation engine to assess product entry into the value chain, providing ranked paths based on resiliency. The results are going to be displayed on an interactive web application, featuring frontend and backend connections, fetching industrial capacities, visualising optimal paths and calculating efficiency and resiliency scores. Under the M4estro framework, resilience strategies are going to be adjusted for each pilot, addressing multiple level manufacturing processes, value networks, human-centricity, and manufacturing assets by focusing on both the component-level development (vertical) and the process chain implementation (horizontal).
Our team will also drive dissemination and communication activities for the project, establish a strong visual identity, utilise diverse channels, and spearhead Intellectual Property Rights management, exploitation planning, and the development of sustainable business models for the M4estro solution.
METAWAVE
The Metawave consortium’s main goal is to implement advanced microwave-based heating systems in high-temperature heating processes in process industries, to enhance process efficiency, reduce energy consumption and lower greenhouse gas emissions. The project also focuses on integrating renewable energy sources through Virtual Power Plant configurations, accompanied by a smart Energy Management System (EMS), and fostering industrial symbiosis, ultimately achieving significant energy savings, emissions reduction, productivity increase, and economic growth for multiple stakeholders.
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Our research team will lead the development of a smart EMS tailored for industrial microwave furnaces, forecasting the power demand and effectively managing the energy sources and storage components. Furthermore, we aim to utilise data-driven techniques, employing Machine Learning and Artificial Intelligence models, to support the development of a Digital Twin for modelling and optimizing the heating process. The findings of the EMS and the Digital Twin will feed a Decision Support System, accompanied by a friendly User Interface, so users can monitor and control of the heating process.
Additionally, we will define the necessary principles and provide support in the selection of scalable architectures, targeting to make the process industries metaverse-ready. Finally, our team will take charge of the exploitation and innovation management of the project, aiming to develop an exploitation roadmap to launch the Metawave technologies to the market.
CARDIMED
Cardimed is a ground-breaking project which aims to unite efforts for Climate Resilience in the Mediterranean region. As part of Cardimed, a digital framework that will be developed to harmonise Nature Based Solutions (NBS) data by engaging communities through smart tools and a multi-stakeholder strategy. Adopting holistic modelling tools for the Water-Energy-Food-Ecosystems (WEFE) Nexus approach, the consortium will aim to address socio-ecological challenges across 9 demo sites, using 47 distinct NBS for a total 83 interventions across sites. Participating regions and communities will establish the Cardimed Resilience Alliance, which will function as a vehicle for the expanding network, via upscaling existing sites and adding new ones. The project expects to have 28 regions and 70 communities by 2030, creating 8000 NBS sector jobs, leveraging 450 million € in climate investments. With 5 defined replicable use cases and the aim to identify 10 more through the project, Cardimed will lead in building a resilient, sustainable future by transforming aspirations into impactful realities.
Follow CARDIMED on LinkedIn and X (Twitter) to stay in touch with project updates.
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CORE will contribute on the refinement activities of user requirements and will develop a cloud-based orchestration middleware for efficient data handling across diverse sources, ensuring security and scalability. We will also focus our efforts on industrial symbiosis through smart water management in the HALCOR demo site, developing designs for NBS and auxiliary solutions such as Digital Twins. Our innovation team will also contribute to the overall exploitation and wider outreach of the project’s outcomes.
SM4RTENANCE
The Sm4rtenance project is a transformative endeavour focused on improving the manufacturing industry through data-driven predictive maintenance and dynamic asset management services. It encompasses various tasks, from harmonising embryonic data spaces to developing innovative service models such as Manufacturing Asset as a Service (MAaaS). Focusing on technical building blocks, data quality standards, digital twin technologies, and AI model development, the projects aims to revolutionise the way the manufacturing sector works. In addition, the project addresses regulatory compliance and GDPR and promotes a collaborative, cross-sector approach.
You can find out more about the project on its dedicated website, or through following our consortium on LinkedIn and X (Twitter).
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Our team will orchestrate and execute a comprehensive commercial exploitation strategy for the consortium, involving the monitoring and planning of commercial viability through market studies and concrete partner plans, integrating the Sm4rtenance solution into various product lines, technology enhancement, academic research, and knowledge transfer within research centers. We will also be involved in collaborative net-zero operation services for asset energy efficiency & low carbon dioxide footprint, with a focus on innovative data spaces.
TERRAVISION
Terravision aims to revolutionise the critical raw materials value chain with its integrated Earth Observation (EO) Mining Services Platform. Supported by four innovation pillars, the Terravision platform will leverage data from the Copernicus satellite, ground radar, drone, and in-situ sensing for comprehensive monitoring. Our consortium will introduce a novel framework for processing multisensory EO data, creating an open and standardised raw material spectral library. The EO services will cater to the mining industry's critical needs, including mapping of materials, mineral exploration, extraction rates, and hazard mapping for proactive risk management. A Green & Resilience Accountability component will be developed, to ensure sustainability by quantifying environmental and socio-economic impacts throughout mining phases. Demonstrated in the EU and beyond, and validated at 6 mining sites, Terravision can pave the way for impactful systemic innovations with EU-wide significance.
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Our team will lead the UX design and user feedback phase, employing a co-creation approach to craft a user-friendly web development tool. This component will focus on delivering a modern web tool for monitoring and analysing data, incorporating advanced technologies and visualisation techniques.
We will also take on exploitation and innovation management activities of the project, analysing Key Exploitable Results (KERs), exploring exploitation routes, and assessing innovation potential. Intellectual Property Rights (IPRs) considerations, value proposition design, and sustainable business models for market-oriented KERs will be key activities driving our team forward. We will aim to address market uptake challenges for by analysing internal and external market barriers, developing roadmaps for commercializing KERs, and conducting competition and SWOT (strengths, weaknesses, opportunities and threats) analysis, to understand market dynamics and the competitiveness of the Terravision solution.
ARGUS
The Argus project aims to address challenges in monitoring remote built heritage assets, with a focus on preventive preservation. It aims to create a novel Digital Twin model for heritage, which will assist in the development of advanced digitization strategies, portable measurement systems, and AI-driven threat identification, assisting informed decision-making for the preventative preservation of heritage assets across Europe.
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Our role in the project is to develop the Argus Decision Support System (DSS), a key component of the project that provides real-time actionable insights on risk assessment and mitigation. The DSS will leverage data collected from various sources to monitor damage thresholds and offer rich visual analytics, infused with predictive capabilities. It will also integrate external information on standards, protocols, and mitigation plans to enhance decision-making and safety measures for existing heritage assets.
GLAS-A-FUELS
The GlaS-A-Fuels project will focus on producing advanced biofuels, such as butanol and hydrogen, from non-land and non-food bio-wastes, to address energy security and environmental concerns.
It will employ a holistic approach, combining recyclable catalysts and a unique photonic glass reactor powered by solar energy to maximize the effectiveness of photo-amplified single-atom catalysts. This innovative process aims to achieve high catalytic performance and challenging reaction intermediates. The project leverages expertise in materials science, catalysis, laser technologies, and process control to develop efficient and sustainable production methods for these advanced biofuels, contributing to the EU's climate-neutral goals by 2050.
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CORE Innovation Centre will have a high level of participation in the project, putting our technical, exploitation, communication and dissemination skills to use. Our engineering team will develop an intelligent software control system to explore various control theories, employing actual and virtual sensors. CORE aims to engineer the lab-scale solar reactor, by integrating it with process monitoring sensors and the control system which is embedded in an IoT-based system, leveraging different wireless technologies for high-level control and real-time monitoring.
Our innovation team will play a key role in maximising the project's innovation potential by evaluating the potential for the development of widely marketable solutions, the readiness of the industry for innovation, and innovation management. This includes creating an innovation roadmap and a detailed exploitation plan, as well as establishing suitable Intellectual Property Rights (IPR) protection methods, based on the technologies developed by the consortium.
Our communication team will collaborate with partners to ensure the consortium successfully reaches out to all stakeholders with its solutions and research finding. We will be leading dissemination and communication efforts, creating a distinct visual identity for the project and transmitting information using all relevant communication channels (social media, industry press, brochures, posters, newsletters, etc.). Additionally, we will hold technology transfer seminars, to present project results and facilitate integration of the Glas-A-Fuels solutions into targeted value chains, focusing on industrial trade fairs and scientific conferences.
The CAPRI project reaches its conclusion
Authors: Konstantina Tsioli, Ioannis Maimaris, Ilia Kantartzi
7th November 2023
After four years, the CAPRI project has come to an end with its final results and an online closing conference. CAPRI’s main goal was to develop cognitive solutions to the Process Industry, in order to facilitate its Digital Transformation. CORE Innovation Centre has been involved in various tasks as part of the project, and we are excited to see project outcomes reach their maturity.
As part of the project, our team developed advanced deep learning models for anomaly detection and Remaining Useful Life estimation of critical components in the asphalt use case. We were also responsible for delivering some of the consortium’s exploitation activities, as well as fully managing all project-related communication tasks.
The goal of CAPRI was to develop, test, experiment and deliver an innovative Cognitive Automation Platform which incorporates cognitive technologies, such as artificial intelligence, machine learning, and advanced automation, to enhance the operations within the Process Industry. Critical outcomes include actions to enhance the flexibility of operations, making the processes more adaptive and responsive, as well as actions to improve operational performance by reducing costs, improving maintenance efficiency, optimising resource utilisation and more.
Deep learning models
The deep learning tools developed by our team have been applied to a use case for asphalt production (EIFFAGE), aiming to reduce maintenance and spare parts costs related to critical operations and to enhance the reliability and robustness of the maintenance system. More specifically:
Anomaly Detection: By leveraging deep learning techniques, this model excels at identifying potential malfunctions in the machinery of asphalt use case. It acts as an ever-watchful guardian, constantly monitoring the baghouse system to alert for anomalies before they become critical issues. This proactive approach allows maintenance to be optimised and to minimise unexpected disruptions.
Remaining Useful Life (RUL) Estimation: Extending the anomaly detection model, our team went one step further by estimating the remaining useful life of critical components. In the EIFFAGE use case, the critical sensor is located at the entrance of the baghouse. This component is essential for maintaining the efficiency and quality of the involved processes. As an outcome, through our RUL estimations, we can accurately predict the time until the next failure of this critical component, offering manufacturers with the foresight needed to plan maintenance activities effectively.
CORE Anomaly Detection model for critical component constant monitoring and for providing possible alerts prior malfunctions.
These solutions have implications for other industries and, once applied, can potentially increase cost efficiency in the steel, aluminum and copper, cement, pharmaceuticals and glass manufacturing industries. More information on the EIFFAGE use case can be found here.
The commercialisation phase
Our innovation team contributed to the exploitation of CAPRI project outcomes by analysing the financial sustainability of the applicable exploitable results, utilising our custom Profit Simulation Tool. This endeavor aimed to gain insights into the financial requirements and resources necessary to introduce the solutions to the market and identify feasible scenarios for the commercialization phase.
This involved estimating Revenues and Costs for a 5-year post-project horizon. The knowledge accumulated throughout the project, which involved the analysis of market conditions and customer segments, was further developed, projecting this analysis into the future for the market. Initially, the analysis focused on customer segments related to the project use case industries, with plans to later expand to include industries identified through the replication analysis. Various scenarios were examined, to pinpoint a pragmatic and viable strategy for partners to implement so they can successfully bring their solutions to market and deliver a sizeable impact for CAPRI on the EU process industry, while also developing their business.
Communication activities
On the communication side, our consortium participated in 32 events over the years, with a total of 13 publications and 16 articles published. The project performed exceptionally well on social media, garnering over 900 followers on X (formerly known as Twitter) and over 1300 on LinkedIn. Additionally, our team ran a YouTube account, which hosts 21 videos with over 2000 views in total. The project website, which was designed by CORE IC and officially launched in the early months of the project, has gained 8400 visits in the three-year run of the project, and it will continue to serve as a central hub for all project deliverables.
It's been a pleasure working with our consortium to deliver cognitive solutions to the European process industry. To stay in touch with the project and its partners, you can visit the dedicated website, or follow the CAPRI accounts on LinkedIn and X.
The Level-Up project has wrapped up with its Final Conference
Authors: Dimitris Eleftheriou, Ioannis Meintanis, Yianna Sigalou
17th October 2023
After four fruitful years, the Level-Up Project has wrapped up with its final deliverables and a Final Conference held in Brussels, Belgium. The project aimed to develop a platform that extends the useful life of major capital investments.
As part of the project, CORE Innovation Centre developed a range of models using ML-based algorithms across different use cases. Our team was also responsible for exploitation and communication activities for the Consortium.
The aim of Level-Up was to offer a scalable platform covering the overall life-cycle of critical role, big industrial machines or their components, starting from the initial digital twins setup to facilitate predictive maintenance, modernisation actions to diagnose and predict the operation of physical assets, even to the refurbishment and re-manufacturing activities towards the end of a machine’s life.
Different machine learning models
The machine learning (ML) based tools developed by our team have been applied to 4 different manufacturing lines (ESMA, LUCCHINI, TOSHULIN, and IPC) at the component, work-station & shopfloor level, with different technologies used depending on the specific needs, and available data for each pilot line.
For ESMA’s cold forming press, our team implemented AI based anomaly detection (AD) algorithms using state-of-the-art Deep Learning (DL) architectures, such as Auto-Encoders and proprietary unsupervised learning algorithms. By utilizing vibration signals and a variety of IoT sensors placed in the equipment, the models can look for patterns in data that indicate failure modes for specific components (e.g. bearings) and provide insights in real-time for the machine operator.
The TOSHULIN production line consists of a large industrial vertical lathe (SKIQ16-v2), with the workpiece clamped on a clamping plate which rotates when in operation. The end-user requirement was to focus on the lubrication system of the cutting tool, to detect anomalies and assess its operation capabilities. To achieve this, we developed a combination of tools, which utilizes a forecasting model to predict the future machine states and the behavior of the oil particles, together with a flexible monitoring mechanism which utilises dynamic thresholds to detect anomalies.
LUCHINI is a full production line for machining railway axles, and for Level-Up we developed an AD procedure using multi-sensorial vibration data. The goal was to facilitate predictive maintenance for the two most critical machines of the production line. The AD procedure is currently at the on-line/production stage, and we continue to monitor the performance and accuracy of the models used.
For IPC/CRF’s pultrusion pilot line, machine learning algorithms for AD and quality control have been developed and integrated with their upgraded monitoring dashboard to assist the operator in decision making and process monitoring procedures.
A go-to-market pathway for the consortium
Our team also led on exploitation activities for the project, to maximise the impact of its results. We developed a detailed exploitation plan for 26 of the project results, across 6 different sectors for our Consortium partners. For each use case, a detailed business plan was developed, which included:
Innovation Management Activities: We analysed the external ecosystem through which Level-Up can evolve, using different strategic tools, like SWOT and Porter’s Five Forces. We analysed the market for each sector, as well as potential market barriers that might slow the adoption of the technologies developed.
Business Models: For each use case, we developed a detailed business model using the Business Model Canva tool, identifying unique selling points, customer personas, costs, potential revenue streams, and key go-to-market activities.
Exploitation Roadmap: We developed detailed exploitation roadmaps and commercialization analyses for the project technologies, accompanied by a 5-year financial plan, which partners can use as a reference in their go-to-market journey.
Reaching out to the community
Finally, CORE Innovation Centre was responsible for handling the communication and dissemination activities of Level-Up.
The project excelled on social media, attracting 904 followers on Twitter and 981 on LinkedIn. During the project, we created 22 videos overall, which collectively received over 3,600 views.
With support from our partners at AIMEN and Innovalia, we carried a series of summer workshops to showcase the project's final results. We also co-organized the project's final conference in Brussels, with more than 70 people in attendance.
Through its four-year run, our Consortium submitted over 7 papers in open access journals, with 4 more pending approval for publication, and attended over 60 industry or scientific events.