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Accelerating Fluids01:17

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Bubble Detection in Gas-Solid Separation Fluidized Beds Based on Deep Learning.

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An improved YOLOv8 model accurately monitors bubbles in dry coal beneficiation, enhancing separation efficiency. Bubble dynamics, particularly bubble area and number, directly predict clean coal yield and ash content.

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Area of Science:

  • * Fluid dynamics and particle technology
  • * Artificial intelligence in process engineering
  • * Materials science and mineral processing

Background:

  • * Bubble dynamics in gas-solid fluidized beds are crucial for stable fluidization and efficient dry coal beneficiation.
  • * Accurate, real-time bubble monitoring is essential for optimizing separation processes.
  • * Existing detection methods often lack the precision or speed required for industrial applications.

Purpose of the Study:

  • * To develop an advanced YOLOv8-based model for precise and real-time bubble detection in dry coal beneficiation.
  • * To investigate the correlation between bubble characteristics and coal separation efficiency.
  • * To provide a robust technical foundation for intelligent dry coal separation systems.

Main Methods:

  • * Implementation of an improved YOLOv8 model incorporating Multi-Head Self-Attention (MHSA), BiFPN-CONCAT, and an Involution-based Decoupled Head.
  • * Experimental validation using 120 datasets to assess detection performance against established models (YOLO series, Faster R-CNN, Mask R-CNN).
  • * Correlation analysis between bubble features (area, number) and beneficiation outcomes (ash content, clean coal yield), followed by regression modeling.

Main Results:

  • * The proposed YOLOv8 model achieved high detection accuracy (precision 99.1%, recall 96.0%, mAP@0.5 95.5%), surpassing existing detectors.
  • * Significant correlations were found: average bubble area negatively correlated with ash content (r = -0.72), and bubble number positively correlated with clean coal yield (r = +0.90).
  • * A regression model based on bubble features predicted separation performance with R² = 0.89.

Conclusions:

  • * The enhanced YOLOv8 model provides accurate, real-time bubble monitoring for dry coal beneficiation.
  • * Bubble characteristics are reliable indicators of separation efficiency, enabling predictive control.
  • * The study offers significant theoretical and technical advancements for intelligent coal processing systems.