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

## TL;DR

This paper introduces a new prediction method for flotation process indicators using a multilayer tree structure belief rule base to improve accuracy and robustness.

## Contribution

The novelty lies in embedding attribute reliability into a multilayer tree structure belief rule base model for enhanced prediction.

## Key findings

- The proposed MTS-BRB-R model improves prediction accuracy in flotation processes.
- Attribute reliability enhances robustness against noise and nonlinear relationships.
- The model was successfully applied to predict tailings silica content in iron ore flotation.

## Abstract

The prediction of key indicators in the flotation process is crucial for optimizing operations, improving quality, and reducing consumption. However, indicator prediction itself suffers from complex nonlinear relationships, difficulties in model construction, and noise interference. To solve the above problems, this paper proposes a new model based on a multilayer tree structure belief rule base (MTS-BRB), termed MTS-BRB with attribute reliability (MTS-BRB-R). First, an initial prediction model is constructed using the MTS-BRB framework. Second, the attribute reliability is embedded into the model structure to enhance the robustness of its inference and prediction accuracy. Finally, the prediction of the tailings silica content in the iron ore flotation process is used as a case study to verify the effectiveness of the proposed model.