AI Intelligence for Smarter E-Waste Recovery
Enhancing material visibility and recovery decisions across e-waste streams to improve yields, strengthen circular performance, and deliver measurable environmental impact.
Led by the University of Westminster. We are seeking pilot partners across the UK recycling infrastructure
Fastest Growing Waste Stream
Electronic waste is the fastest-growing waste category globally, placing increasing pressure on recovery systems.
Critical Materials at Risk
Rare earth elements and high-value metals are routinely lost due to limited material visibility and suboptimal sorting decisions.
Net-Zero Imperative
Smarter upstream recovery reduces embedded carbon loss and supports national net-zero and circular economy targets.
AI-Enabled Recovery Within Existing Infrastructure
RECLAIM-WISE integrates advanced AI detection and decision-support models directly into operational recycling systems. Rather than replacing infrastructure, the platform enhances existing workflows with real-time material intelligence.
By improving visibility into material composition and recovery pathways, operators can increase yield, reduce loss of rare earth elements, and make carbon-conscious processing decisions.
Increased rare-earth material recovery
Data-driven operational optimisation
Reduced carbon leakage
Why Smarter Recovery Is Urgent
E-waste volumes continue to rise year-on-year while demand for critical materials intensifies. At the same time, recycling systems remain largely manual, reactive, and data-poor.
The transition to a circular, low-carbon economy requires more than incremental improvements — it requires intelligent, upstream decision support embedded within recovery systems.
AI-enabled material intelligence represents a step-change in how e-waste is processed, valued, and reintegrated into industrial supply chains.
Inside Reclaim Wise Technology
How RECLAIM-WISE Works
RECLAIM-WISE introduces intelligent upstream decision support into electronic waste processing to increase material recovery and reduce carbon loss across recycling systems.
Intelligent Material Detection
Machine learning models analyse complex e-waste streams to identify high-value materials within mixed waste flows.
AI-Driven Decision Support
Advanced analytics recommend optimal recovery pathways, improving yield and operational performance.
Seamless System Integration
Designed to integrate with existing treatment facilities and equipment without major infrastructure disruption.
Who We Work With
We collaborate with organisations across the e-waste value chain to embed AI-driven material intelligence into operational recovery systems.
E-Waste Recyclers & AATFs
Increase material recovery rates, enhance reporting accuracy, and improve operational margins.
Local Authorities & Councils
Strengthen circular economy strategies with data-backed material intelligence and carbon reporting.
Industrial Metal Recovery Partners
Improve feedstock quality, traceability, and access to high-value recovered materials.
Start a Collaboration Conversation
RECLAIM-WISE is an applied AI research initiative led by the University of Westminster.
The project is led by Dr Dipankar Sengupta, Senior Lecturer in Health Data Analytics, bringing expertise in AI modelling, sustainability systems, and circular economy innovation.
We are actively seeking pilot partners, research collaborators, and strategic industry stakeholders to advance intelligent e-waste recovery across the UK.
