Timely and accurate recognition of flotation working conditions plays a crucial role in stabilizing process indicators and formulating production operation strategies. Soft sensing methods based on deep learning allow for automatic learning of features from input froth images directly, and is considered a promising flotation monitoring approach. However, the high demand for a large number of labeled samples severely hinders the application of such methods because acquiring froth videos is easily in flotation plants, but manually adding labels to video frames is often expensive. Therefore, a representation-enhanced semi-supervised learning method for flotation working condition recognition is proposed in this paper. Firstly, based on the consistency assumption, a student-teacher network is developed. It leverages massive unlabeled froth image data by ensuring the consistency of model outputs under variations in the input space. Secondly, to guide the student-teacher network learn class-aware cluster representation from unlabeled data, a novel prototypical contrastive learning method with hard negative mixing is proposed. Comparative and ablation experiments on industrial flotation dataset show the effectiveness of the proposed method.
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