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The design of copper flotation process based on multi-label classification and regression.

Haipei Dong1,2, Fuli Wang3, Dakuo He4

  • 1Qian Xuesen College, Qingdao Huanghai University, Qingdao, China.

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|July 18, 2025
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This study introduces a novel AI approach for copper flotation process design, effectively handling both classification and regression tasks. The method significantly enhances prediction accuracy for improved resource utilization in mining.

Keywords:
Copper flotationDomain knowledgeLabel correlationMulti-label

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Area of Science:

  • Artificial Intelligence in Mining Engineering
  • Process Design and Optimization

Background:

  • Copper flotation process design is crucial for resource utilization and cost reduction in mining.
  • Previous work successfully applied multi-label classification to backbone process design but not cleaning/scavenging.
  • Cleaning and scavenging process design presents a multi-label regression challenge.

Purpose of the Study:

  • To develop a unified method for simultaneously handling multi-label classification and regression in copper flotation process design.
  • To improve the prediction accuracy and robustness of AI models for flotation process design.

Main Methods:

  • Integration of multi-label classification and regression capabilities.
  • Application of weighted samples based on prediction error (Adaboost-inspired).
  • Introduction of a label uncertainty coefficient for enhanced robustness.
  • Implementation of bootstrap aggregating to mitigate overfitting in small sample datasets.

Main Results:

  • The proposed method demonstrates significant superiority in copper flotation process design compared to previous approaches.
  • Ablation experiments confirm the effectiveness of individual improvements (weighting, uncertainty coefficient, bootstrap aggregating).
  • The approach successfully addresses both classification and regression aspects of flotation process design.

Conclusions:

  • The developed AI method offers a powerful, unified solution for complex copper flotation process design.
  • The findings have significant implications for the engineering application of artificial intelligence in the mining industry.
  • The improvements enhance prediction accuracy, robustness, and generalization for flotation process optimization.