Accurate and timely investigation to concentrate grade and recovery is a premise of realizing automation control in a froth flotation process. This study seeks to use deep learning technologies modeling a manufacturing flotation process, forecasting the concentrate purities for iron and the waste silica. Considering the size and temporality of engineering data, we adopted a long short-term memory to form the core part of the deep learning model. To perform this process, 23 variables reflecting a flotation plant were monitored and collected hourly over a half year time span, then wrangled, split, and restructured for deep learning model use. A deep learning model encompassing a stacked long short-term memory architecture was designed, trained, and tested with prepared data. The model’s performance on test data demonstrates the capability of our proposed model to predict real-time concentrate purities for iron and silica. Compared with a traditional machine model typified by a random forest model in this study, the proposed deep learning model is significantly more competent to model a manufacturing froth flotation process. Expected to lay a foundation for realizing automation control of the flotation process, this study should encourage deep learning in mineral processing engineering.
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This study was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Collaborative Research and Development (CRD) Grant (NSERC RGPIN-2019-04572). And also, supports from Chinese Scholarship Council were gratefully acknowledged.
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Pu, Y., Szmigiel, A. & Apel, D.B. Purities prediction in a manufacturing froth flotation plant: the deep learning techniques. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-04773-2
- Froth flotation
- Deep learning
- Long short-term memory
- Concentrate purity