Real-time Coal and Gangue Identification powered by Image Segmentation

Based on Patent Research | CN-115018794-B (2023)

Accurately identifying coal and gangue (waste rock) during fully mechanized mining is crucial for operational efficiency. Current methods often lack the speed and precision needed, leading to lower coal quality and higher disposal costs. Image segmentation, which classifies individual pixels, provides a robust solution. It precisely differentiates coal from gangue by analyzing visual and infrared data at the pixel level. This enables real-time separation, improving coal quality and reducing waste disposal costs.

SOLUTION OVERVIEW

Automated Ore Sorting Replaces Manual

Image segmentation technology directly addresses the challenges of inefficient and imprecise material differentiation in mining and quarrying. This computer vision approach enables pixel-level classification, meticulously distinguishing valuable coal from waste rock (gangue). The process begins with capturing visual and infrared data from material streams. This data is then analyzed pixel by pixel, allowing the system to accurately identify and delineate distinct regions of coal and gangue based on their unique characteristics. The output provides precise, real-time insights for automated sorting.

This capability significantly enhances operational efficiency by supporting automated separation systems, leading to improved coal quality and reduced gangue disposal costs. The technology can integrate seamlessly into existing conveyor belt operations, providing continuous monitoring and classification. Consider it akin to a highly specialized sorting system on a mining conveyor, where intelligent vision instantly highlights and directs every piece of coal and gangue to its proper destination. This automation optimizes resource recovery and streamlines processing, bringing substantial operational improvements to extraction workflows.

HOW IT WORKS

Identifying Coal from Mining Images

Capturing Material Stream Data

The system continuously captures high-resolution visual and infrared images of materials as they move along the conveyor belt. This dual-spectrum data acquisition provides comprehensive information about the unique physical and chemical characteristics of coal and gangue, essential for accurate differentiation.

Analyzing and Segmenting Materials

These captured images undergo necessary pre-processing steps before being analyzed pixel by pixel using advanced image segmentation algorithms. This detailed process precisely differentiates and delineates distinct regions of valuable coal from waste rock (gangue) based on their identified properties.

Classifying and Reporting Insights

Based on the precise segmentation, the system accurately classifies each identified material as either coal or gangue, simultaneously calculating the gangue mixing rate in real-time. These immediate, actionable insights are then utilized to support automated sorting systems, significantly improving coal quality and reducing disposal costs.

Potential Benefits

Elevate Coal Product Quality

Real-time pixel-level segmentation precisely separates valuable coal from gangue. This ensures a higher purity product, meeting market demands more effectively.

Decrease Waste Disposal Expenses

By accurately identifying and diverting waste rock, the system significantly minimizes the volume of material requiring costly disposal. This directly lowers operational expenditures.

Boost Material Sorting Accuracy

Meticulous pixel-level classification, using visual and infrared data, boosts accuracy in distinguishing coal from gangue. This overcomes the precision limitations of current sorting methods.

Maximize Valuable Resource Yield

Automated, precise differentiation ensures that valuable coal is not discarded with waste, optimizing resource utilization. This leads to a greater yield from mined materials.

Implementation

1 Install Sensing Hardware. Mount visual and infrared cameras over conveyor belts. Ensure stable power, lighting, and network connectivity for data capture.

2 Configure AI Software. Install and configure the image segmentation software. Load pre-trained models and define initial parameters for material detection.

3 Calibrate and Test. Process initial material samples to calibrate segmentation accuracy. Validate gangue mixing rate calculations against known standards.

4 Integrate with Sorting. Connect the AI output, including classification and real-time insights, to automated sorting mechanisms or control systems.

5 Monitor and Optimize. Continuously monitor system performance and data quality. Periodically update models to maintain high accuracy and efficiency.

Source: Analysis based on Patent CN-115018794-B "Coal and gangue identification system and method based on visible light/infrared image" (Filed: February 2023).