重庆大学 机械与运载工程学院
TP391.4; TD952
中央高校基本科研业务费专项资金(No. 2022CDJKYJH024)和重庆市自然科学基金面上项目 (No. 2022NSCQ-MSX1629)
College of Mechanical Engineering,Chongqing University
the Fundamental Research Funds for the Central Universities (No. 2022CDJKYJH024) and the Natural Science Foundation of Chongqing (No. 2022NSCQ-MSX1629)
在选矿行业中,准确地预测精矿品位可以帮助工程师提前进行工艺参数调整,提高浮选性能。但在实际选矿过程中,采集数据存在样本量少、维度高、时序相关性复杂等问题,限制了精矿品位的预测精度。针对小样本数据的预测问题,提出了一种将Wasserstein生成对抗网络(wasserstein generative adversarial network, Wasserstein GAN)和长短期记忆网络(long short-term memory, LSTM)相结合的时间序列数据生成模型LS-WGAN,主要利用LSTM网络来获取选矿数据中的时间相关性,再通过Wasserstein GAN 网络生成与原始数据分布相似的样本进行数据增强；为了更加准确的预测精矿品位,建立了浮选预测模型C-LSTM,并基于真实泡沫浮选工艺数据实验验证了所提出方法的预测准确性。
In 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 limited sample sizes, high-dimensional data and complex temporal correlations in actual mineral processing. A time-series data generation model called LS-WGAN is proposed according to the problem of prediction for small sample data, which combines wasserstein generative adversarial network (Wasserstein GAN) and long short-term memory (LSTM) neural network. LSTM network is mainly used to capture the time correlation in mineral processing data, then Wasserstein GAN is used to generate samples similar to the original data distribution for data augmentation. In order to improve concentrate grade prediction accuracy, a mineral processing prediction model called C-LSTM is established, the prediction accuracy of the proposed method is verified by experiments based on real froth flotation process data.
