Project cooperationUpdated on 14 July 2025
Digital Twin for Regional and Cross-Border Plastics Cycles (REPLAY)
About
The REPLAY project addresses the central challenge of inefficient industrial plastic waste recycling, which often leads to valuable materials being lost from the circular economy through thermal recovery. This issue particularly affects small and medium-sized enterprises (SMEs) that lack economically viable solutions for smaller waste streams, and is compounded by a fragmented data landscape that hinders effective collaboration. Our goal is to overcome these barriers by developing a digital platform for regional plastics cycles, enhancing planning security and enabling the creation of robust, cost-effective recycling networks.
While the digital platform is initially focused on regionally connecting actors and material flows, it is designed from the outset to be scalable and interoperable across national borders. By spanning the regional idea across countries, REPLAY enables cross-border transparency and cooperation—maximizing synergy potentials, improving access to higher quantities and qualities of recyclate, and making regional circular economies more resilient to disruptions. This cross-border perspective is especially valuable for SMEs near borders or in highly integrated industrial areas, who can thus participate in larger, more efficient recycling networks.
Our innovative approach combines a Digital Twin, Artificial Intelligence, and a secure data platform to create a comprehensive model of the circular economy. By using simulations, we will build a realistic Digital Twin to model, evaluate, and optimize various logistics concepts and industrial symbioses. AI-powered models will be developed to provide crucial decision support, such as forecasting the supply and demand of recyclates and optimizing transport logistics. A decentralized data platform, based on standards like DIN SPEC 91446 and technologies like W3C DIDs, will ensure interoperable and secure information exchange not just within regions, but also seamlessly across national boundaries—supporting harmonized data, traceability, and compliance for international supply chains.
We are a consortium of three German technical universities (THWS, THN, THI) and a diverse group of industry partners, from global corporations to specialized SMEs, ensuring our solutions are both scientifically advanced and practically applicable. The project actively seeks additional partners from other countries, enabling real cross-border pilots and demonstration cases. New and international partners will thus gain the opportunity to shape, test, and benefit from emerging circular economy solutions under both regional and cross-border conditions. The project will culminate in a functional demonstrator that quantifies the tangible benefits of the system for both regional and cross-border collaborations, including increased recycling quotas, CO₂ savings, and enhanced operational efficiency for all involved partners.
Stage
- Early idea
Topic
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Data technologies | Interoperability of CVC-relevant data ecosystems, quality assurance and traceability across systems
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Assistance and Expert systems
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Data technologies | Design of an adaptable Digital Product Pass:
- Enabling technologies | Industry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chains
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
Type
- Consortium seeks Partners
Attached files
Organisation
Similar opportunities
Project cooperation
- Early idea
- Expertise offered
- Consortium seeks Partners
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Enabling technologies | Industry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chains
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
Laura Bies
Lab Lead Smart Quality at August-Wilhelm Scheer Institut
Saarbrücken, Germany
Project cooperation
Digital ecosystem for circular value creation
- Early idea
- Consortium seeks Partners
- Data technologies | Assistance and Expert systems
- Data technologies | Design of an adaptable Digital Product Pass:
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Approaches to support SME fully exploit the value of existing CVC-related data
- Enabling technologies | (Advanced/smart) Sensors, e.g., enabling materials, components and product flows measurement
- Data technologies | Interoperability of CVC-relevant data ecosystems, quality assurance and traceability across systems
- Enabling technologies | Industry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chains
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)
Yoav Nahshon
Team Leader Materials Informatics at Fraunhofer IWM
Freiburg, Germany
Project cooperation
Al and Circular Economy in Coating and Insulation
- Consortium seeks Partners
- Data technologies | Assistance and Expert systems
- Enabling technologies | Network design of reverse supply chains
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
Kaan AKSOY
Advanced Technologies and Sustainability R&D Department at BETEK BOYA VE KİMYA SANAYİ A.Ş
İSTANBUL, Türkiye