Intelligent detection model of flotation tailings ash based on CNN-BP
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摘要: 尾煤灰分是浮选系统的重要生产指标，不仅可以反映当前浮选系统运行工况和精煤采出率，对浮选智能化控制也有重要意义。针对现有基于图像的浮选尾煤灰分检测方法特征提取不全面、模型精度不足的问题，提出了一种基于卷积神经网络（CNN）−反向传播（BP）的浮选尾煤灰分智能检测方法。构建了CNN初步预测与BP神经网络补偿预测相结合的浮选尾煤灰分智能检测模型。通过CNN提取矿浆图像特征数据，初步预测尾煤灰分，然后将图像灰度特征数据和彩色特征数据作为BP补偿模型的输入，以初步预测值与真实值的差值为输出，最终将初步预测值与补偿预测值相加，得到浮选尾煤灰分。实验结果表明：磁力搅拌器的转子为小转子、转速为500 r/min、光照强度为12 750 Lux条件下矿浆搅拌充分，图像质量最好；与CNN模型及极限学习机(ELM)模型相比，CNN−BP模型预测精度最高，误差波动范围最小，预测误差范围为−2%~+2%；CNN−BP模型的均方根误差（RMSE）为0.770 5，决定系数为0.997 4，平均绝对误差（MAE）为0.557 2%，表明其精度高、效果好、泛化性强，可以满足现场生产检测要求。Abstract: The tailings ash is an important production index of flotation systems. It not only reflects the current operating conditions of flotation system and clean coal recovery, but also has important significance for intelligent flotation control. The existing image-based detection method of flotation tailings ash has the problems of incomplete feature extraction and insufficient model precision. In order to solve the above problems, an intelligent detection method of flotation tailings ash based on convolutional neural network (CNN) - back propagation (BP) is proposed. An intelligent detection model of flotation tailings ash is constructed by combining CNN preliminary prediction and BP neural network compensation prediction. The pulp image feature data is extracted through CNN to preliminarily predict the tailings ash. The image gray feature data and color feature data are used as input to the BP compensation model. The difference between the preliminary prediction value and the actual value is used as output. Finally, the preliminary prediction value and the compensation prediction value are added to obtain the flotation tailings ash. The experimental results show that when the rotor of the magnetic stirrer is small, the rotation speed is 500 r/min, and the light intensity is 12 750 Lux, the pulp is fully stirred, and the image quality is the best. Compared with the CNN model and extreme learning machine (ELM) model, the CNN-BP model has the highest prediction precision, the smallest error fluctuation range. The prediction error is within the range of −2% to +2%. The root mean square error (RMSE) of the CNN-BP model is 0.7705, the determination coefficient is 0.9974, and the mean absolute error (MAE) is 0.5572%. This indicates that its high precision, good effect and strong generalization can meet the requirements of on-site production testing.
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图 5 基于CNN−BP的浮选尾煤灰分智能检测模型
Figure 5. Intelligent detection model of flotation tailings ash based on CNN-BP
表 1 不同光照强度下图像灰度均值差
Table 1 Image gray mean difference under different light intensity
光照强度/Lux 灰度均值差 光照强度/Lux 灰度均值差 9 000 10.55 12 750 209.84 9 750 14.38 13 500 206.42 10 500 45.22 14 250 200.65 11 250 135.15 15 000 195.70 12 000 178.12 
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表 2 CNN训练参数
Table 2 CNN training parameters
参数 设定值 优化算法 sgdm 基础学习率 0.0001 学习率变化期数 20 学习率变化指数 0.1 验证迭代次数 30 单位批量次数 4 训练设备 GPU
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表 3 模型预测结果评价
Table 3 Evaluation of model prediction results
模型 MAE/% RMSE R2 ELM 1.495 9 1.783 2 0.984 3 CNN 0.959 0 1.201 0 0.993 8 CNN−BP 0.557 2 0.770 5 0.997 4
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