Purities prediction in a manufacturing froth flotation plant: the deep learning techniques
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ORIGINAL ARTICLE
Purities prediction in a manufacturing froth flotation plant: the deep learning techniques Yuanyuan Pu1,2
•
Alicja Szmigiel2 • Derek B. Apel2
Received: 26 November 2019 / Accepted: 3 February 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Froth flotation Deep learning Long short-term memory Concentrate purity
1 Introduction Froth flotation is an extensively used physiochemical mineral processing technology for separating particles of valuable and unwanted minerals, with the fact that different minerals have a different physicochemical surface characteristic, either hydrophobic or hydrophilic [1, 2]. Timely investigation to concentrate grade and recovery with online monitoring or estimation in a froth flotation plant is important to processing engineers, which is fundamental to Yuanyuan Pu and Alicja Szmigiel have contributed equally to this work. & Yuanyuan Pu [email protected] & Derek B. Apel [email protected] 1
College of Resources and Safety Engineering, State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, People’s Republic of China
2
School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
process control and optimization. Online monitoring requires purchasing and maintaining costly and sophisticated instruments, which necessitates development of models for concentrate grade and recovery estimation based on processing parameters. Some difficulties, such as a lack of comprehensive knowledge of the physicochemical rules for flotation subprocess and the need of deducing and solving mathematic equations for a moving boundary (colle
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