Upstream process condition monitoring for froth flotation based on feature performance evaluation and parameter-mapped GRNN

Abstract

Timely and reliable estimation of the upstream process condition in flotation process is significant for whole flotation operations. An upstream process condition monitoring model based on feature performance evaluation and parameter-mapped generalized regression neural network (GRNN) is proposed to evaluate visual feature performance and improve the generalization ability of ensemble models for zinc flotation. Firstly, different visual features are extracted, and the froth video representation is obtained. Then, feature performance evaluation is constructed, considering the correlation between different visual features and flotation indicators, and stability of feature representation ability vary. Meanwhile, feature performance evaluation indexes are calculated introducing a scale factor to integrate the Pearson correlation with feature stability coefficient. Finally, a parameter-mapped GRNN, whose base model parameters are mapped into a certain interval, is designed to monitor the upstream process condition based on the feature performance evaluation index-weighted feature vector. Experiments using real-world lead–zinc flotation data were implemented to show the potential of the feature performance evaluation and effectiveness of the proposed model.