基于深度学习的浮选精矿品位预测方法

矿浆品位是浮选工艺中关键参数之一,其对于指导生产,节约药剂,控制产品质量和提高回收率等方面都起着非常关键的作用。为了在线预测浮选精矿品位，解决荧光分析仪检测滞后的问题，研究出了一种不需要主观提取特征的基于深度学习的精矿品位在线预测模型，模型的输入为浮选泡沫图像序列、原矿品位值和尾矿品位值，输出为精矿品位值，属于回归问题。对比了主干网络分别为VGG-16，ResNet-50和MobileNet-V2时预测结果的差异，实验结果显示VGG-16的预测精度和鲁棒性最好,平均预测精度达到12.48%。

Prediction method of flotation concentrate grade based on deep learning

The pulp grade is one of the key parameters in the flotation process, which plays a key role in guiding production, saving chemicals, controlling product quality and improving recovery. In order to predict the flotation concentrate grade online and solve the problem of the detection lag of the fluorescence analyzer, a deep learning-based online prediction model of concentrate grade is developed that does not require subjective extraction of features. The value and tailings grade value are input, and the output concentrate grade value is a regression problem. Comparing the differences in the prediction results when the backbone network is VGG-16, ResNet-50 and MobileNet-V2, the experimental results show that VGG-16 has the best prediction accuracy and robustness.The average prediction accuracy is 12.48%.