<p>Performance comparison of different models.</p>

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.

浮选工艺关键指标的预测，对于优化生产操作、提升产品质量与降低能耗均至关重要。然而，指标预测本身存在复杂非线性关联、模型构建难度大且易受噪声干扰等难题。为解决上述问题，本文提出一种基于多层树结构置信规则库（multilayer tree structure belief rule base，MTS-BRB）的新型模型，将其命名为带属性可靠性的多层树结构置信规则库（MTS-BRB-R）。首先，基于MTS-BRB框架构建初始预测模型；其次，将属性可靠性嵌入模型结构，以提升其推理鲁棒性与预测精度；最后，以铁矿石浮选工艺尾矿二氧化硅含量预测为案例，验证所提模型的有效性。