JUSTRA Optical Sorting
JUSTRA AI Optical Sorter
JUSTRA AI Sorting Machine belongs to MSW Technology Group. It specializes in optical sorting of municipal solid waste (recyclables, plastics, etc.).
| Processing capacity | 2-10 tons/hour |
| Recognition accuracy | >99.5% |
| Sorting purity | >98% |
| Suitable materials | PET, HDPE, PP, paper, metals, etc. |
JUSTRA AI Sorting Robot
JUSTRA AI Sorting Machine belongs to MSW Technology Group. It specializes in optical sorting of C&D Waste (recyclables, plastics, wood etc.).
| Processing capacity | 2-5 tons/hour |
| Recognition accuracy | >98% |
| Sorting purity | >96% |
| Suitable materials | wood, metals, gypsum board, plastics, etc. |
Technology Principles
AI Optical Sorting Machine Principles
AI optical sorting machine is an intelligent sorting system based on artificial intelligence and advanced optical sensing technology. The system collects optical characteristic data of materials through high-resolution cameras and various spectral sensors, including color, shape, texture, and near-infrared spectral characteristics. This data is transmitted to the built-in AI algorithm model for processing and analysis.
Core Technical Indicators
Deep learning algorithms trained on large datasets can identify subtle differences between different materials, enabling accurate classification even in complex mixed material streams. Compared with traditional sorting methods, AI optical sorting machines have higher recognition accuracy and adaptability, capable of handling multiple material types and complex sorting tasks.
AI + Hyperspectral Optical Sorting Machine Principles
AI + hyperspectral optical sorting technology is a cutting-edge breakthrough in the field of optical sorting, combining hyperspectral imaging technology with artificial intelligence algorithms. Hyperspectral imaging can acquire spectral information of objects in hundreds of continuous narrow bands, forming a complete spectral "fingerprint" that traditional RGB cameras cannot achieve.
Hyperspectral sorters collect continuous spectral data of materials in the visible to near-infrared region (400-2500nm), building a detailed spectral feature library for each material. AI algorithms perform dimensionality reduction processing and feature extraction on these high-dimensional spectral data to establish material identification models.
In practical applications, hyperspectral cameras continuously scan rapidly passing materials, acquiring complete spectral curves for each pixel. The AI system compares these spectral curves in real-time with pre-stored spectral databases to accurately identify material types and composition, enabling accurate separation even for materials with similar colors but different compositions (such as different types of plastics).
This technology is particularly suitable for sorting complex waste streams, capable of identifying materials that are difficult to distinguish by traditional methods, such as PET vs. PVC, different types of paper, wood vs. composite materials, greatly improving sorting purity and recovery rates.
Hyperspectral Sorting Advantages
| Advantage | Description | Impact |
|---|---|---|
| Material Identification Capability | Can identify 300+ different materials, including materials with similar colors but different compositions | Enhanced sorting accuracy |
| Sorting Purity | 35-60% improvement in sorting purity compared to traditional methods | Higher quality output |
| Recovery Rate | Material recovery rate up to 95-98%, reducing resource waste | Increased profitability |
Performance Comparison Analysis
Compared with traditional sorting methods and manual sorting, AI optical sorting machines have significant advantages in efficiency, accuracy, and economic benefits.
| Indicator | Manual Sorting | Traditional Mechanical Sorting | AI Optical Sorting |
|---|---|---|---|
| Processing Capacity (tons/hour) | 0.5-1 | 3-5 | 2-10 |
| Sorting Accuracy | 85-90% | 90-95% | 98-99.5% |
| Sorting Purity | 80-85% | 85-90% | 95-98% |
| Operating Cost ($/ton) | 120-150 | 80-100 | 40-60 |
| Labor Requirements | High (8-10 people) | Medium (3-5 people) | Low (1-2 people) |
How AI Optical Sorting Machine Works?
1 High-Speed Detection
Multispectral sensors capture particle attributes at 5,000+ scans per second, including RGB color, NIR reflectance, or X-ray density for comprehensive material analysis.
2 AI-Powered Analysis
Deep learning algorithms instantly compare sensor data against defect libraries, achieving 99.9% recognition accuracy for impurities as small as 0.5mm.
3 Precise Ejection
AI-nozzle arrays with 0.1ms response time remove defects using compressed air, achieving 99.5% purity in processed materials without product damage.
4 Industry-Proven Efficiency
Processes 2-10 tons/hour while reducing waste by 30-90% compared to manual sorting, with ROI achievable in 6-18 months across various applications.
Technical Specifications
| Parameter | Specification | Description |
|---|---|---|
| Processing Capacity | 2-10 T/H | Depending on material and sorting requirements |
| Recognition Accuracy | >99.5% | For particles above 0.5mm |
| Sorting Purity | >98% | Target material recovery rate |
| Power Consumption | 5-12 kW | Adjustable based on throughput |
| Compressed Air | 6-8 bar | Pressure requirement |
| Operating Temperature | 0-45°C | Environmental adaptability |
Application Scenarios
These advanced sorting solutions find applications across numerous industries. In waste sorting and recycling, particularly in MRF (Material Recovery Facility) projects, AI optical sorting machines play a crucial role. They can efficiently separate various recyclable materials from mixed municipal solid waste, such as different types of plastics, paper, metals, and glass.
Municipal Solid Waste
Separating PET, HDPE, PP plastics and paper, metals, etc.
Construction & Demolition Waste
Sorting wood, metals, gypsum board, plastics, etc.
Plastic Recycling
Precise separation of different plastic types
Mining Industry
Ore pre-concentration, waste rock removal
Food Processing
Removing foreign materials, defective products
E-Waste Recycling
Sorting PCBs, metals, plastics, etc.
MRF Project Application Details
In Material Recovery Facilities (MRF), AI optical sorting machines are core equipment for efficient waste classification and resource recovery. Through multi-stage sorting processes, material recovery rates of up to 95% can be achieved.
MRF Project Benefit Analysis
| Benefit Type | Key Metrics | Impact |
|---|---|---|
| Economic Benefits |
|
Higher profitability and faster ROI |
| Environmental Benefits |
|
Sustainable operations and compliance |
About Us
MSW Technology Group is a leading provider of sorting solutions in China. The company's core products include sensor-based intelligent sorting equipment: AI sorting machine, color sorter, NIR sorter, X-ray sorter, and laser sorter.
These sorting solutions are widely used across various industries including grain processing, food manufacturing, mining, resource recycling, energy, and environmental protection.
JUSTRA and MSW Technology Group are partnering to empower the global resource recycling industry. We are committed to providing innovative sorting solutions that enhance efficiency, boost profitability, and shape a more sustainable world.
Our Expertise
| AI Optical Sorting Technology | |
| Hyperspectral Imaging | |
| Multi-sensor Fusion | |
| Industrial Automation | |
| Waste Management Solutions |