Interpretable Prediction Modeling for Froth Flotation via Stacked Graph Convolutional Network

Abstract

Real-time prediction of key quality variables based on data-driven soft sensor modeling is an important way to monitor flotation status and product quality in the froth flotation process. However, the existing data-driven methods have limitations in terms of nonlinear feature extraction and interpretability. In addition, the prevalent correlations between process variables can help improve the model interpretability and feature extraction of the model, but there are still challenges in exploring the potential correlations between process variables due to the complexity of the industrial process mechanism and the presence of high noise in process data. These relationships can be abstracted as edges between nodes in a graph representation. To fully explore the deep process-related structural features of data, a novel stacked graph convolutional network (S-GCN) is proposed. First, S-GCN aggregates the features of neighboring nodes by an adaptive adjacency matrix to achieve the extraction of structural features among process variables. Then, the labeled quality variable data detected by laboratory assay analysis are utilized to pre-train the network layer by layer to obtain better initial parameters and adjacency matrices for quality prediction. Finally, the prediction model is constructed based on the learned parameters to explore the interpretability of model and perform prediction tasks. Experimental results on real industrial potassium chloride froth flotation process data show a significant improvement in the prediction accuracy and interpretability of the proposed method.