Dynamic Hypergraph Neural Networks for Flotation Condition Recognition Based on Group-View Composite Features

Metal mineral flotation plays a critical role in the mining industry, where accurate recognition of flotation conditions is essential for optimizing operations and enhancing mineral recovery rates. Although extensive research has focused on observable froth characteristics, a substantial gap remains in understanding the underlying high-dimensional manifold structures within features extracted from flotation video sequences. Moreover, conventional single-view feature extraction techniques often suffer from noise caused by mutual interference, reducing the robustness and reliability of extracted features. To address these challenges, we propose group-view multipath residual neural network (GVResNet)-EvolveHGNN, a novel framework for robust feature extraction and spatial-temporal modeling in flotation video sequences. The GVResNet module adopts a GVResNet structure with a cross-attention mechanism to enhance feature stability and suppress noise. GVResNet uses average pooling (AP) of sequential images and cross-attention to compute the significance of each image feature representation, and groups features based on their relevance and distinctions. In addition, to capture evolving relationships within high-dimensional feature spaces, we introduce EvolveHGNN—an evolving hypergraph neural network (HGNN). Unlike static models, EvolveHGNN adopts the recurrent neural network (RNN) to dynamically adjust hypergraph parameters over time, effectively capturing the temporal evolution of flotation sequences. Ablation studies reveal that our dynamic hypergraph model significantly outperforms conventional multilayer perceptron (MLP) models, which fail to account for spatial structures, and also surpasses static hypergraph algorithms, achieving respective accuracy improvements of 1.41% and 1.35%. This proposed framework offers more robust feature representations, serving as a powerful tool for the accurate identification of the working conditions in mineral processing.