Grün HDF-R Series AI-Driven Sorting System

In the further value extraction from mixed non-ferrous metal scrap, the industry faces a sorting challenge: after shredding, various metal fractions exhibit diverse colors, textures, sheen, and morphological features, which are difficult for manual operators to consistently capture and judge on high-speed production lines.

The Grün HDF-R Series is an intelligent machine-vision-based sorting system tailored for this exact scenario. It employs high-resolution RGB (HR-RGB) VIS imaging and proprietary AI deep learning algorithms to perform real-time scanning and surface feature analysis on mixed non-ferrous scrap. Coupled with high-performance air ejectors, it efficiently identifies and recovers target metal fractions from the material stream within milliseconds, based on surface feature differences.

Empowered by AI-driven surface feature recognition algorithms, the system can conduct deep learning training based on real production data, continuously optimizing the machine vision detection accuracy. This means the equipment’s performance is not fixed at the time of factory shipment; instead, it continuously adjusts and improves with the accumulation of production data, delivering stable and reliable sorting performance to help non-ferrous scrap recyclers significantly boost recycling efficiency and commercial value.

Technical Features

● High-Resolution RGB (HR-RGB) VIS Imaging

Material surface features are clearly captured, providing reliable image input for AI algorithms and ensuring the accuracy and stability of surface defect recognition.

● Innovative Dual-Belt Sorting Structure

High-sensitivity sorting is performed on the upper level to establish extremely high purity of the main material stream; secondary re-inspection of upper-level rejects is conducted on the lower level, ensuring effective recovery of high-value fractions mixed within.

● Proprietary AI Deep Learning Algorithms

Trained against comprehensive identification and sorting standards, the system uses deep learning to replicate the surface-feature judgment logic of manual picking. The AI model can be continuously optimized with new production data, driving ongoing improvements in sorting accuracy.

● High-Intensity LED Illumination

A constant imaging environment is maintained with clear and stable images, unaffected by external light changes, guaranteeing consistent detection results across different time periods and batches.

● High-Performance Air Ejector

Equipped with in-house, high-power, high-speed air ejectors, the system delivers high-precision material rejection and ensures long-term stable operation even under high-throughput sorting conditions.

● Multi-Specification Model Portfolio

Available in two models with belt widths of 600 mm  and 1200 mm, to meet varying throughput requirements.

Typical Application Scenarios and Benefits

✦ Metal Recycling Plants: Reduce smelting impurities & energy consumption

✩ Extract copper/aluminum from mixed scraps (copper, aluminum, zinc, stainless steel) with efficiency and high purity.

✦ Auto Dismantlers: Improves recovery rates and profitability

✩ Extract high-value copper wires from shredder residue to boost profit per ton, distinguishing copper (reddish-brown) from aluminum (silver-white).

✦ Battery Recyclers: Avoids hydrometallurgy risks

✩ Recover high-value electrode by detection to copper (purple-red) and aluminum (silver-white), targeting unpeeled areas.

✦ E-Waste Processors: Boost revenue, cut labor cost and reduce landfill waste

✩ Extract gold-plated contacts from discarded mobile phone boards to increase refining profits.

✦ Appliance Recycling: Purely physical sorting, no burning/chemicals

✩ Sort copper tubes (reddish-brown) and aluminum heat sinks (silver-white) from refrigerators/AC units.

✩ Separate copper windings (shiny)from silicon steel (matte) in washing machine motors mix.

Specifications