Programme time: 13-Oct | 14:00 | 75 min
Room: D. Luis Room (Industry 5.0)

Moderator: TBC
Speakers
•    TBC

Full description and objectives

Industrial equipment embodies significant economic, material and knowledge value, yet many assets are replaced before their useful life is fully exploited.

Emerging advances in Data, AI, digital twins and intelligent automation create new opportunities to extend equipment lifecycles through re-use, remanufacturing, re-build and upcycling strategies.

This workshop will investigate how AI and data-driven approaches can support circularity across three interconnected levels. The first level focuses on intelligent devices and machines, addressing condition monitoring, predictive maintenance and equipment upgrade opportunities. The second level explores digital twins, lifecycle intelligence, Digital Product Passports and AI-enabled decision support. The third level addresses dismantling, material recovery, component reuse and upcycling enabled by data analytics, automation and robotics.

Using a World Café methodology, participants will rotate across the three thematic tables, enabling multidisciplinary exchange between researchers, industry practitioners and policy stakeholders.

The workshop objectives are to:

• Identify barriers and opportunities for circular industrial equipment.

• Map key data and AI enablers across the three circularity levels.

• Identify research, innovation, standardisation and deployment gaps.

• Foster collaboration between ADR communities and industrial stakeholders.

The workshop will produce a concise report summarising challenges, gaps, recommendations and potential next steps for the ADR community.

Why you want to join?

This workshop directly addresses the ADR Forum themes of AI and Data by exploring how data-driven intelligence, digital twins and AI technologies can support sustainable industrial transformation by enabling circular economy practices for industrial equipment.

It highlights the role of AI and data across the full circular lifecycle of industrial assets, from intelligent operation and maintenance to remanufacturing, dismantling and material reuse. Data from equipment operation, maintenance records, inspection systems, digital twins and product passports can support evidence-based decisions on whether equipment should be re-used, repaired, remanufactured, re-built, upgraded or recycled.

AI can transform this information and data into actionable intelligence through condition assessment, remaining useful life estimation, anomaly detection, decision support, optimisation of remanufacturing processes, and identification of upcycling opportunities.

The workshop will explore how AI- and data-driven methods can reduce waste, increase equipment lifetime, improve resource efficiency, and create new business models around circular industrial assets. It also promotes collaboration between manufacturing, data spaces, digital twins, robotics and sustainability communities, creating a bridge between technological innovation and circular economy objectives.

Running agenda