Fusion of deep feature and apparent feature for flotation grade prediction based on apparent information guidance encoder–decoder network

Grade prediction is a critical component of the froth flotation production process. Recent grade prediction methods are typically based on deep features or apparent features. Deep feature based approaches achieve superior prediction accuracy, but the non-interpretability of these features hinders parameter tuning when process conditions change. Apparent features offer a more intuitive representation of froth grade, but the sparsity of apparent features limits the predictive capability in grade prediction. Hence, to improve the predictive performance and adaptability of the model under fluctuating conditions, it is essential to integrate apparent feature information into the deep representations. In this study, the feature fusion model for grade prediction based on apparent information guidance encoder–decoder network is proposed. Firstly, to integrate apparent features and deep features, a feature fusion module for merging apparent features and deep features is designed. Additionally, to guide the extraction rules of deep features and enhance the interpretability of deep features, an apparent information guidance module based on the variational autoencoder (VAE) is proposed. Moreover, to verify that the latent variables of the guidance module can represent the apparent characteristics of the input froth image, a froth image reconstruction module based on transposed convolutional layer is organized to generate corresponding froth images according to apparent features. Ablation experiments validated the effectiveness of the proposed method, and comparative experiments with other mainstream deep models demonstrated the superior grade prediction performance of our proposed method on the flotation dataset.