Flotation Grade Monitoring Using Feed Grade Refined Embedding and Nonoverlapped Patch Encoding

Accurate grade monitoring in froth flotation is crucial for improving process efficiency and mineral recovery. Current grade monitoring models typically use mean video features and fail to capture feature variations within individual videos. Furthermore, they usually embed the most recently available feed grade into the visual feature vector without providing a refined embedding of feed grade. Therefore, we propose a flotation grade monitoring model with feed grade refined embedding (FGRE) and nonoverlapping patch encoding (NOPE). First, we develop an FGRE module to generate fine-grained feed grade representations. This module extracts the time series of critical feed grade elements by physical topology of the flotation process, and it employs a grouped weight-sharing time-lagged-recurrent neural network (RNN) to fuse multisource temporal data. Then, we introduce an RNN with NOPE to encode video patches and extract dynamic information from each froth video. This network enables the capture of subtle visual feature variations within each individual video. After that, we implement a multivideo temporal extraction strategy to capture dynamic temporal features across multiple videos for grade monitoring. Experiments are conducted on a real-world zinc flotation process. Our proposed approach can be integrated into most existing flotation grade monitoring models, significantly enhancing their monitoring performance.