Data-Efficient Soft Sensing Learning for Flotation Process Monitoring

Froth flotation is an important mineral processing technique for separating and recovering valuable minerals from ore. However, developing image-based monitoring models for industrial flotation is hindered by the high cost and scarcity of labeled data. To overcome this challenge, we propose a memory-network-based data-efficient framework for predicting key performance indicator (KPI) in flotation monitoring. Our approach integrates two key innovations: 1) a novel froth image representation model that captures both macroscopic visual patterns of the froth layer and correlations among localized froth patterns across the image and 2) a hybrid self-supervised and semi-supervised learning strategy that leverages the short-term stationarity (STS) of flotation processes for label-free pretraining, followed by memory attention distillation (MAD) to refine predictions using limited labeled data. Experiments on an industrial flotation dataset comprising 32 971 input-output pairs demonstrate that our method surpasses state-of-the-art semi-supervised regression techniques by 7.97% in R-squared accuracy and achieves performance comparable to fully supervised models while using 80% fewer labeled samples.