Causality-Driven Direction-Aware Graph Attention Network for Key Feature Selection in Flotation Process

In flotation process, froth appearance features play a critical role in condition recognition. However, due to the high dimensionality and complex coupling among froth image features, it is difficult to accurately identify key features that truly reflect flotation conditions. Moreover, most existing feature selection methods ignore the directional and causal interactions among features, resulting in limited interpretability and suboptimal performance. In this paper, a Causal-Driven Direction-Aware Graph Attention Network is proposed for froth image feature selection in flotation processes. Granger causality analysis is first employed to characterize temporal causal relationships among multidimensional froth image features, based on which a directed causal feature graph is constructed. Then, a direction-aware graph attention network is designed to model feature interactions under causal constraints, ensuring that information aggregation follows physically meaningful directions. In addition, a group-wise competitive feature masking mechanism and a correlation-constrained multi-objective loss function are introduced to suppress feature redundancy. The proposed method is validated on industrial flotation froth image datasets, and experimental results demonstrate that the CD-DA-GAT achieves higher recognition accuracy and better feature interpretability compared with conventional feature selection methods.