Abstract
In froth flotation, the tailings grade and concentrate grade are the two key performance indexes. At present, the monitoring models of these two key grades mostly use the froth image or video from a flotation cell. However, flotation cells are closely related and coupled seriously. It is difficult to use a froth image or video from a flotation cell to represent the concentrate or tailings grade. Therefore, an encoder-decoder and Siamese time series network (ES-net) is proposed. First, an encoder-decoder (ED) model is designed to predict target grade (i.e., the zinc tailings or concentrate grade) by the video feature sequence of the first rougher and the measured target grade sequence. Meanwhile, a Siamese time series and difference network (STS-D net) is constructed to predict the target grade by the video feature sequences of target flotation cell (i.e., the last scavenger or cleaner) at current and previous moments and the previously measured target grade. After that, a multi-task learning strategy is proposed to integrate the ED model and STS-D net. Experiments show that the proposed ES-net can effectively integrate multiple froth visual features from different flotation cells and obtain more accurate concentrate and tailings grades than the existing models.
Published Version
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