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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[1] 桂夏辉,邢耀文,曹亦俊,等. 低品质煤泥浮选过程强化研究进展及其思考[J]. 煤炭学报,2021,46(9):2715-2732.GUI Xiahui,XING Yaowen,CAO Yijun,et al. Recent advances and thinking in process intensification of low quality coal slime flotation[J]. Journal of China Coal Society,2021,46(9):2715-2732. [2] 周娟华,李太友. 《智能化选煤厂建设通用技术规范》标准解读[J]. 煤炭加工与综合利用,2022(1):71-78.ZHOU Juanhua,LI Taiyou. Interpretation of General technical specification for construction of intelligent coal preparation plant[J]. Coal Processing & Comprehensive Utilization,2022(1):71-78. [3] 王然风,高建川,付翔. 智能化选煤厂架构及关键技术[J]. 工矿自动化,2019,45(7):28-32.WANG Ranfeng,GAO Jianchuan,FU Xiang. Framework and key technologies of intelligent coal preparation plant[J]. Industry and Mine Automation,2019,45(7):28-32. [4] GB/T 212—2008煤的工业分析方法[S].GB/T 212-2008 Industrial analysis method of coal[S]. [5] 张泽琳,杨建国. 煤灰分在线检测方法及设备[J]. 选煤技术,2012(2):59-63,72.ZHANG Zelin,YANG Jianguo. Research on process and equipment of coal ash content on-line detection[J]. Coal Preparation Technology,2012(2):59-63,72. [6] 张勇,王介生,王伟,等. 浮选生产过程经济技术指标的软测量建模[J]. 控制工程,2005(4):346-348,378.ZHANG Yong,WANG Jiesheng,WANG Wei,et al. Soft-sensor modeling for economy and technology indexes in flotation process[J]. Control Engineering of China,2005(4):346-348,378. [7] 汤化明,王玲,杨国华,等. 人工智能在矿物加工技术中的应用与发展[J]. 金属矿山,2022(2):1-9. doi: 10.19614/j.cnki.jsks.202202001TANG Huaming,WANG Ling,YANG Guohua,et al. Application and development of artificial intelligence in mineral processing technology[J]. Metal Mine,2022(2):1-9. doi: 10.19614/j.cnki.jsks.202202001 [8] 魏凌敖. 基于机器视觉的煤泥浮选加药控制系统研究[D]. 徐州: 中国矿业大学, 2020.WEI Ling'ao. Research of coal slime flotation dosing control system based on machine vision[D]. Xuzhou: China University of Mining and Technology, 2020. [9] 桂卫华,阳春华,徐德刚,等. 基于机器视觉的矿物浮选过程监控技术研究进展[J]. 自动化学报,2013,39(11):1879-1888. doi: 10.3724/SP.J.1004.2013.01879GUI Weihua,YANG Chunhua,XU Degang,et al. Machine-vision-based online measuring and controlling technologies for mineral flotation-a review[J]. Acta Automatica Sinica,2013,39(11):1879-1888. doi: 10.3724/SP.J.1004.2013.01879 [10] 谭利平,张泽琳,赵伟,等. 基于机器视觉的矿物浮选泡沫监控研究进展[J]. 矿业研究与开发,2020,40(11):123-130.TAN Liping,ZHANG Zelin,ZHAO Wei,et al. Research status of mineral flotation foam monitoring based on machine vision[J]. Mining Research and Development,2020,40(11):123-130. [11] 曹文艳,王然风,樊民强,等. 基于半监督聚类的煤泥浮选泡沫图像分类方法[J]. 工矿自动化,2019,45(7):38-42,65.CAO Wenyan,WANG Ranfeng,FAN Minqiang,et al. Coal slime flotation foam image classification method based on semi-supervised clustering[J]. Industry and Mine Automation,2019,45(7):38-42,65. [12] 王光辉. 煤泥浮选过程模型仿真及控制研究[D]. 徐州: 中国矿业大学, 2012.WANG Guanghui. Model simulation and control of coal slurry flotation process[D]. Xuzhou: China University of Mining and Technology, 2012. [13] 高博. 基于图像灰度特征的浮选尾矿灰分软测量研究[D]. 徐州: 中国矿业大学, 2016.GAO Bo. Study on soft-sensing model for ash measurement of floatation tailings based on gray features of image[D]. Xuzhou: China University of Mining and Technology, 2016. [14] 包玉奇,杨洁明. 基于机器视觉的浮选尾矿灰分检测[J]. 煤炭技术,2015,34(9):313-316.BAO Yuqi,YANG Jieming. Flotation tailings ash detection based on machine vision[J]. Coal Technology,2015,34(9):313-316. [15] 王靖千,王然风,付翔,等. 基于彩色图像处理的浮选尾煤灰分软测量研究[J]. 煤炭工程,2020,52(3):137-142.WANG Jingqian,WANG Ranfeng,FU Xiang,et al. Study on soft sensing of ash content in flotation tail coal based on color image processing[J]. Coal Engineering,2020,52(3):137-142. [16] WANG Guichao,FENG Dongxia,NGUYEN A,et al. The dynamic contact angle of a bubble with an immersed-in-water particle and its implications for bubble-particle detachment[J]. International Journal of Mineral Processing,2016,151:22-32. doi: 10.1016/j.minpro.2016.04.001 [17] 周飞燕,金林鹏,董军. 卷积神经网络研究综述[J]. 计算机学报,2017,40(6):1229-1251.ZHOU Feiyan,JIN Linpeng,DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers,2017,40(6):1229-1251. [18] 焦李成,杨淑媛,刘芳,等. 神经网络七十年:回顾与展望[J]. 计算机学报,2016,39(8):1697-1716.JIAO Licheng,YANG Shuyuan,LIU Fang,et al. Seventy years beyond neural networks:retrospect and prospect[J]. Chinese Journal of Computers,2016,39(8):1697-1716. [19] 周博文,王然风,付翔. 基于浮选尾煤图像的灰分检测试验研究[J]. 矿业研究与开发,2021,41(8):172-177.ZHOU Bowen,WANG Ranfeng,FU Xiang. Experimental research on ash content detection based on coal flotation tailings images[J]. Mining Research and Development,2021,41(8):172-177. [20] SCHMIDHUBER J. Deep learning in neural networks:an overview[J]. Neural Networks,2015,61:85-117. doi: 10.1016/j.neunet.2014.09.003 [21] 徐睿,梁循,齐金山,等. 极限学习机前沿进展与趋势[J]. 计算机学报,2019,42(7):1640-1670.XU Rui,LIANG Xun,QI Jinshan,et al. Advances and trends in extreme learning machine[J]. Chinese Journal of Computers,2019,42(7):1640-1670. -
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