CIRCULAR-AI: Energy-Efficient AI-Driven Hybrid Waste Sorting and Circular Resource Recovery

Project CIRCULAR-AI aims to transform industrial waste sorting lines into low-emission, energy-efficient recovery hubs through a modular retrofit hybrid architecture. Modern municipal and commercial sorting facilities still face high energy consumption, severe material purity bottlenecks, and heavy reliance on hazardous manual labor.

CIRCULAR-AI introduces an integrated, three-tier automated separation process designed to maximize circularity:
1. Multi-Class Edge Perception: Real-time tracking, 90+ material classification, and vision-based weight estimation powered by optimized, low-power edge computer vision algorithms.
2. High-Speed Pneumatic Bulk Sorting: Millisecond predictive actuation synchronizing air-jet manifolds for high-throughput bulk separation (>85% recovery rate) with minimal compressed-air energy losses.
3. Precision Dynamic Robotic Picking: Secondary robotic extraction equipped with adaptive grippers and dynamic visual servoing, targeting >95% sorting purity for high-value recyclables (e.g., PET, aluminum, rigid polymers).

In line with Eurogia's decarbonization goals, the project integrates Green AI principles to prune and quantize neural models for runtime edge energy reduction, coupled with deterministic industrial PLC control (OPC UA), geospatial digital twin tracking, and real-time software carbon footprint certification.

The system will be validated in an operational end-user recycling plant to demonstrate a 25% increase in secondary raw material recovery and a 30% reduction in processing energy consumption.