Intelligent automated sorting system for automotive shredder light fraction
Advanced sensor-based sorting system using XRF, NIR, and AI-driven vision analytics to identify and separate scrap materials in recycling processes. The approach combines spectroscopy and imaging to reduce false positives and defects, improving yield accuracy and operational efficiency while integrating with existing sorting operations.
Overview
The intelligent automated sorting system is designed to improve recycling of automotive shredder light fractions. It uses multiple sensor modalities, including X-ray fluorescence (XRF) and near-infrared (NIR) spectroscopy, together with AI-driven vision analytics, to identify and sort diverse scrap materials. The system aims to minimize false positives and defects and to integrate with existing operations to enhance yield accuracy and operational efficiency.
Technical specifications
Key features:
- Multi-sensor modalities: Utilizes XRF and NIR for material identification.
- AI-driven analytics: Uses vision systems to classify ferrous, non-ferrous, coated, and hazardous materials.
- Seamless integration: Designed to integrate with existing recycling/sorting processes to improve throughput and efficiency.
The system employs NIR spectra and RGB cameras to distinguish polymers and elastomers, while an induction coil and XRF sensors separate ferrous and non-ferrous metals. The AI component uses machine learning to improve sorting accuracy and to build a sensor-linked database for process optimization.
Technology readiness level
This technology is at TRL 7, having been demonstrated in an operational environment. Future steps include scaling up to industrial scale and integrating results into existing processes.
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