A new multilayer tree structure belief rule base-based prediction method for key indicators of flotation process.

Area of Science:

• Chemical Engineering
• Data Science
• Materials Science

Background:

• Flotation process indicator prediction is vital for operational efficiency and resource management.
• Existing prediction models face challenges due to complex nonlinearities, model construction difficulties, and noise interference.

Purpose of the Study:

• To propose a novel prediction model addressing the limitations of current methods in flotation processes.
• To enhance the robustness and accuracy of indicator prediction in industrial applications.

Main Methods:

• Development of a multilayer tree structure belief rule base (MTS-BRB) framework.
• Integration of attribute reliability into the MTS-BRB model, creating the MTS-BRB-R.
• Case study involving prediction of tailings silica content in iron ore flotation.

Main Results:

• The proposed MTS-BRB-R model demonstrates improved inference and prediction accuracy.
• Validation through a case study confirms the model's effectiveness in a real-world industrial scenario.
• The attribute reliability enhancement significantly boosts model performance.

Conclusions:

• The MTS-BRB-R model offers a robust solution for predicting key indicators in flotation processes.
• This approach effectively handles complex nonlinear relationships and noise, improving operational outcomes.
• The study validates the practical applicability and benefits of the MTS-BRB-R in the iron ore industry.