Digitalization is essential for industrial competitiveness. A pillar of Industry 4.0, Digital Twins and digital intelligence enhance energy efficiency and production optimization. The DENIM project by R2M Solution developed a free platform to support the energy transition in manufacturing. In addition to AI-driven workforce training, R2M contributed to the development of a business plan for the platform’s commercialization. Learn more about DENIM!
The Role of Digitalization in Industrial Energy Efficiency
Manufacturing is one of the most energy-intensive sectors globally. According to EUROSTAT, in 2022, it still accounted for 25.1 % of final energy consumption in Europe. While Industry 4.0 has introduced new opportunities for energy efficiency, fully integrating digitalization and energy management remains a challenge.
DENIM (Digital Intelligence for collaborative ENergy Management in Manufacturing) was an EU-funded Horizon 2020 project, concluded in December 2024, with R2M Solution actively involved in exploitation and scalability activities. The project’s goal was to develop an interoperable digital platform for collaborative energy management in manufacturing, leveraging Digital Twins for monitoring and optimizing energy consumption.
DENIM’s objectives
DENIM helps manufacturers adopt a sustainable and efficient approach to energy management by transforming machine data into actionable insights. Using Big Data, IoT, Cloud Computing, Digital Twins, Machine Learning, and Modelling & Simulation, DENIM bridges the gap between technological innovation and tangible results in energy savings, cost reduction, and sustainability.
DENIM’s Four Key Innovation Areas
1. DENIM Digital Platform – Data Integration and Sharing
The platform, based on Edge and Cloud Computing, ensures secure access to energy data and facilitates integration with existing infrastructures. A common semantic model enhances interoperability across manufacturing systems.
2. Digital Twin – Energy Modeling and Optimization
Digital models accurately replicate machine operations, enabling predictive simulations and energy-saving identification. Automated energy consumption verification ensures efficiency targets are met.
3. Decision Support System (DSS) – Process Optimization
DENIM integrates a DSS based on LCA (Life Cycle Assessment) and LCCA (Life Cycle Cost Assessment) to support automated energy audits, energy flow optimization, and waste reduction.
4. Digital Skills – Workforce Training and Development
To maintain competitiveness and sustainability, DENIM developed an innovative training strategy that assesses a company’s digital maturity and provides Industry 4.0 and energy management training.
The Role of Digital Twins in the DENIM Project
In Industry 4.0, Digital Twins (DTs) are essential for energy efficiency. A DT is a digital replica of a physical asset, using real-time data to create a dynamic virtual model. Unlike Digital Shadows, DTs enable bidirectional communication, allowing simulations and real-time energy optimization.
DENIM’s Digital Twin: Structure and Functionality
DENIM’s Digital Twin consists of four key components:
- Data Monitoring & Acquisition: Sensors collect real-time data for an accurate system representation.
- Digital Shadow: A digital replica reflects the asset’s real-time status.
- Virtual Simulations: Behavior models test different energy optimization scenarios.
- Decision Support System (DSS): Data analysis suggests energy efficiency strategies.
This integration transforms raw data into actionable insights, optimizing energy use and reducing inefficiencies.
Key Benefits of DENIM’s Approach
✅ Enhanced Energy Management: Identifies energy waste and inefficiencies.
✅ Real-time Optimization: Predictive simulations support sustainable production strategies.
✅ DSS Integration: Evaluates environmental and economic impacts.
✅ Increased Reliability & Preventive Maintenance: Detects machine anomalies.
Pilot Applications
DENIM’s Digital Twins were tested in four pilot sites:
- DePuy Synthes (Ireland): Monitoring for renewable energy integration.
- SIDENOR & CIE Galfor (Spain): Simulation of steelmaking and forging processes.
- Gorenje Orodjarna (Slovenia): Predictive models for optimizing energy use.
- MET snc (Italy): Edge Intelligence for real-time energy control.
Results
Challenges and Future Perspectives
Implementing Digital Twins requires:
- Managing large data volumes with scalable platforms.
- Integrating with legacy systems without disrupting production.
- Ensuring cybersecurity to protect collected data.
With advances in AI and edge computing, Digital Twins will become even more precise and efficient in energy management.
DENIM’s Impact and Benefits
The project exceeded expectations, achieving energy and CO₂ reductions, enhancing European industrial competitiveness, and supporting renewable energy integration. DENIM is a tangible example of Industry 5.0, where digitalization, sustainability, and collaboration drive the transformation of manufacturing.
🔗 Learn more: https://www.denim-fof.eu
