重庆大学学报2024，Vol.47Issue(9)：81-90,10.DOI:10.11835/j.issn.1000-582X.2023.107
基于Wasserstein GAN数据增强的矿物浮选纯度预测
Froth flotation purity prediction based on Wasserstein GAN data augmentation
摘要
Abstract
In the mineral processing industry,accurately predicting concentrate grade can help engineers adjust process parameters in advance and improve flotation performance. However,the prediction accuracy of concentrate grade has been restricted by small sample sizes,high-dimensional data,and complex temporal correlations in actual mineral processing. To address the predication challenges associated with small sample data,a time-series data generation model called LS-WGAN is proposed,which combines the Wasserstein generative adversarial network (Wasserstein GAN) and long short-term memory (LSTM) neural network. The LSTM network is mainly used to capture the time correlation in mineral processing data,while the Wasserstein GAN generates samples similar to the original data distribution for data augmentation. To improve the prediction accuracy of the concentrate grade,a mineral processing prediction model called C-LSTM is established. The prediction accuracy of the proposed method is verified through experiments based on real froth flotation process data.关键词
精矿品位预测/Wasserstein生成对抗网络/LSTM/数据增强/深度学习Key words
prediction of concentrate grade/Wasserstein generative adversarial network/long short-term memory (LSTM)/data augmentation/deep learning分类
信息技术与安全科学引用本文复制引用
吴浩生,江沛,王作学,杨博栋..基于Wasserstein GAN数据增强的矿物浮选纯度预测[J].重庆大学学报,2024,47(9):81-90,10.基金项目
中央高校基本科研业务费专项资金资助项目(2022CDJKYJH024) （2022CDJKYJH024）
重庆市自然科学基金面上项目(2022NSCQ-MSX1629).Supported by the Fundamental Research Funds for the Central Universities(2022CDJKYJH024),and the Natural Science Foundation of Chongqing(2022NSCQ-MSX1629). （2022NSCQ-MSX1629）
