Research Progress of Flotation Foam State Informatization Based on the Background of Intelligent Beneficiation

ZHANG Yanbing; MA Yiwen; LIU Xiaobo; SUN Xin; YAO Fuxing; ZHENG Mengke; SUN Jinghui

doi:10.13779/j.cnki.issn1001-0076.2024.08.015

2025 Vol. 45, No. 1

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Article navigation > Conservation and Utilization of Mineral Resources > 2025 > 45(1): 93-100

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ZHANG Yanbing, MA Yiwen, LIU Xiaobo, SUN Xin, YAO Fuxing, ZHENG Mengke, SUN Jinghui. Research Progress of Flotation Foam State Informatization Based on the Background of Intelligent Beneficiation[J]. Conservation and Utilization of Mineral Resources, 2025, 45(1): 93-100. doi: 10.13779/j.cnki.issn1001-0076.2024.08.015

Citation:

ZHANG Yanbing, MA Yiwen, LIU Xiaobo, SUN Xin, YAO Fuxing, ZHENG Mengke, SUN Jinghui. Research Progress of Flotation Foam State Informatization Based on the Background of Intelligent Beneficiation[J]. Conservation and Utilization of Mineral Resources, 2025, 45(1): 93-100. doi: 10.13779/j.cnki.issn1001-0076.2024.08.015

Research Progress of Flotation Foam State Informatization Based on the Background of Intelligent Beneficiation

1.
School of Mining engineering, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China
2.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
3.
Intelligent Mine Research Institute of University of Science and Technology Liaoning, Anshan 114051, Liaoning, China

More Information

Corresponding author: MA Yiwen, me-myw@ustl.edu.cn

Received Date: 02 July 2024

Publish Date: 15 February 2025

Abstract

Abstract

Mineral resources are the foundation of social and economic development. According to the goal of realizing high−quality development of mineral resources in the new period of China, it is necessary to build green and highly efficient mines by using information and digital technology. Intelligent beneficiation is a part of intelligent mine, and its implementation is based on the informatization and digitalization of mineral processing. Taking foam flotation as an example, the common methods of foam state informatization were combed, and the digital applications of foam state information were further described, then the development and promotion direction of intelligent flotation process was discussed. The aim is to facilitate the advancement of the research process related to advanced technologies in the field of intelligent mineral processing.
- intelligent beneficiation /
- flotation foam /
- informatization /
- digitization

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References

[1]	中国矿产资源报告2022[J]. 自然资源情报, 2023(1): 2. Google Scholar China mineral resources report 2022[J]. Natural Resource Information, 2023(1): 2. Google Scholar
[2]	邵安林, 刘畅, 岳星彤, 等. 新时期我国矿产资源行业高质量发展路径[J]. 金属矿山, 2024(1): 2−6. Google Scholar SHAO A L, LIU C, YUE X, et al. Path of high−quality development of China's mineral resources industry in the new era[J]. Metal Mine, 2024(1): 2−6. Google Scholar
[3]	柳小波, 张兴帆, 曲福明, 等. 我国冶金行业智慧矿山建设路径探索与实践[J]. 金属矿山, 2024(1): 45−54. Google Scholar LIU X B, ZHANG X F, QU F M, et al. Exploration and engineering practice of the development path for smart mines in China′s metallurgical industry[J]. Metal Mine, 2024(1): 45−54. Google Scholar
[4]	中华人民共和国自然资源部. 智能矿山建设规范: DZ/T 0376—2021[S]. 北京: 地质出版社, 2021. Google Scholar Ministry of Natural Resources of the People′s Republic of China. Specification for intelligent mine construction: DZ/T 0376—2021[S]. Beijing: Geology Press, 2021. Google Scholar
[5]	祝晋, 刘威, 高立强. 智能选矿厂的建设探索与实践[J]. 有色金属(选矿部分), 2023(1): 121−126. Google Scholar ZHU J, LIU W, GAO L Q. Exploration and practice of intelligent concentrator construction[J]. Nonferrous Metals (Mineral Processing Section), 2023(1): 121−126. Google Scholar
[6]	刘道喜. 基于浮选泡沫图像颜色特征提取的矿浆品位建模及工程实现[D]. 沈阳: 东北大学, 2024. Google Scholar LIU D X. Pulp grade modeling and engineering realization based on color feature extraction of flotation foam images[D]. Shenyang: Northeastern University, 2024. Google Scholar
[7]	李佳俊. 基于图像处理的辉铜矿浮选泡沫图像研究[D]. 黄石: 湖北师范大学, 2023. Google Scholar LI J J. Study on froth image of chalcocite flotation based on image processing[D]. Huangshi: Hubei Normal University, 2023. Google Scholar
[8]	MOOLMAN D W, AlRICH C, VAN DEVENTER J S J. The interpretation of flotation froth surfaces by using digital image analysis and neural networks[J]. Chemical Engineering Science, 1995, 50(22): 3501−3513. doi: 10.1016/0009-2509(95)00190-G CrossRef Google Scholar
[9]	MOOLMAN D W, ALRICH C, SCHMITZ G, et al. The interrelationship between surface froth characteristics and industrial flotation performance[J]. Minerals Engineering, 1996, 9(8): 837−854. Google Scholar
[10]	HARGRAVE JM, MILES N J, HALL S T. The use of grey level measurement in predicting coal flotation performance[J]. Minerals Engineering, 1996, 9(6): 667−674. doi: 10.1016/0892-6875(96)00054-4 CrossRef Google Scholar
[11]	WOODBURN E T, AUSTIN L G, STOCKTON J B. A froth based flotation kinetic model[J]. Chemical Engineering Research & Design, 1994, 72(2A): 211−226. Google Scholar
[12]	VENTURA−MEDINA E, CILLIERS J J. Calculation of the specific surface area in flotation[J]. Minerals Engineering, 2000, 13(3): 265−275. doi: 10.1016/S0892-6875(00)00006-6 CrossRef Google Scholar
[13]	VENTURA−MEDINA E, CILLIERS J J. A model to describe flotation performance based on physics of foams and froth image analysis[J]. International Journal of Mineral Processing, 2002, 67: 79−99. doi: 10.1016/S0301-7516(02)00038-8 CrossRef Google Scholar
[14]	WANG W, BERGHOLM F, YANG B. Froth delineation based on image classification[J]. Minerals Engineering, 2003, 16: 1183−1192. doi: 10.1016/j.mineng.2003.07.014 CrossRef Google Scholar
[15]	VINCENT L, SOLILLE P. Watershed in digital spaces: An efficient algorithm based immersion simulations[J]. IEEE Trans. PA−MI, 1991, 13(6): 583−598. doi: 10.1109/34.87344 CrossRef Google Scholar
[16]	SAMEER H M, DEE J B, MARTIN C H. The use of the froth surface lamellae burst rate as a flotation froth stability measurement[J]. Minerals Engineering, 2012, 36(10): 152−159. Google Scholar
[17]	XU C H, GUI W H, YANG C H, et al. Flotation process fault detection using output PDF of bubble size distribution[J]. Minerals Engineering, 2012, 26: 5−12. doi: 10.1016/j.mineng.2011.09.012 CrossRef Google Scholar
[18]	CHEN X F, GUI W H, YANG C H, et al. Adaptive image processing for bubbles in flotation process[J]. Measurement & Control, 2011, 44(4): 121−125. Google Scholar
[19]	HAMARNEH G, LI X. Watershed segmentation using prior shape and appearance knowledge[J]. Image & Vision Computing, 2009, 27(1): 59−68. Google Scholar
[20]	GAO H, LIN W. Marker−based image segmentation relying on disjoint set union[J]. Signal Processing Image Communication, 2006, 21(2): 100−112. doi: 10.1016/j.image.2005.06.008 CrossRef Google Scholar
[21]	GONZALEZ R C, WOODS R E. Woods digital image processing: 2nd edition[M]. Beijing: Publishing House of Electronics Industry, 2011. Google Scholar
[22]	林小竹, 谷莹莹, 赵国庆. 煤泥浮选泡沫图像分割与特征提取[J]. 煤炭学报, 2007(3): 304−308. doi: 10.3321/j.issn:0253-9993.2007.03.019 CrossRef Google Scholar LIN X Z, GU Y Y, ZHAO G Q. Image segmentation and feature extraction of slime flotation foam[J]. Journal of China Coal Society, 2007(3): 304−308. doi: 10.3321/j.issn:0253-9993.2007.03.019 CrossRef Google Scholar
[23]	李怡. 铜浮选过程泡沫图像特征提取及工况识别方法研究[D]. 沈阳: 东北大学, 2019. Google Scholar LI Y. Study on foam image feature extraction and working condition recognition method in copper flotation process[D]. Shenyang: Northeastern University, 2019. Google Scholar
[24]	周开军. 矿物浮选泡沫图像形态特征提取方法与应用[D]. 长沙: 中南大学, 2010. Google Scholar ZHOU K J. Morphological feature extraction method and application of mineral flotation foam image[D]. Changsha: Central South University, 2010. Google Scholar
[25]	廖一鹏, 王卫星. 结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割[J]. 光学精密工程, 2016, 24(10): 2589−2600. doi: 10.3788/OPE.20162410.2589 CrossRef Google Scholar LIAO Y P, WANG W X. Flotation bubble image segmentation combined with multi−scale edge enhancement and adaptive valley bottom detection[J]. Optics and Precision Engineering, 2016, 24(10): 2589−2600. doi: 10.3788/OPE.20162410.2589 CrossRef Google Scholar
[26]	NING Z. An image segmentation algorithm for flotation foam of copper and molybdenum ore[J]. Computer Knowledge and Technology, 2014, 10(12): 2875−2877. Google Scholar
[27]	王宇龙. 基于机器视觉的煤泥浮选泡沫分类研究[D]. 太原: 太原理工大学, 2022. Google Scholar WANG Y L. Study on classification of coal slime flotation foam based on machine vision[D]. Taiyuan: Taiyuan University of Technology, 2022. Google Scholar
[28]	温智平. 基于深度学习的煤泥浮选过程灰分预测与系统控制研究[D]. 徐州: 中国矿业大学, 2023. Google Scholar WEN Z P. Research on ash prediction and system control of coal slime flotation process based on deep learning[D]. Xuzhou: China University of Mining and Technology, 2023. Google Scholar
[29]	胡椰清. 基于泡沫图像特征的钨浮选精矿品位预测研究[D]. 长沙: 中南大学, 2023. Google Scholar HU Y Q. Prediction of tungsten flotation concentrate grade based on foam image features[D] . Changsha: Central South University, 2023. Google Scholar
[30]	陈宁, 黄璐, 桂卫华, 等. 泡沫图像统计建模与恒常颜色校正算法研究[J]. 控制理论与应用, 2016, 33(5): 613−620. doi: 10.7641/CTA.2016.50783 CrossRef Google Scholar CHEN N, HUANG L, GUI W H, et al. Research on statistical modeling and constant color correction algorithm of foam image[J]. Control Theory and Applications, 2016, 33(5): 613−620. doi: 10.7641/CTA.2016.50783 CrossRef Google Scholar
[31]	LIU J, GUI W, TANG Z, et al. Recognition of the operational statuses of reagent addition using dynamic bubble size distribution in copper flotation process[J]. Minerals Engineering, 2013, 45: 128−41. doi: 10.1016/j.mineng.2013.02.003 CrossRef Google Scholar
[32]	何桂春, 黄开启. 浮选指标与浮选泡沫数字图像关系研究[J]. 金属矿山, 2008, 37(8): 96−101. Google Scholar HE G C, HUANG K Q. Study of the relation between flotation indexes and froth digital images[J]. Metal Mine, 2008, 37(8): 96−101. Google Scholar
[33]	HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural Features for Image Classification[J]. IEEE Transactions on Systems, Man and Cybernetics, 1973, 3(6): 610−621. Google Scholar
[34]	刘文礼, 路迈西, 王凡, 等. 煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别[J]. 化工学报, 2003, 54(6): 830−835. doi: 10.3321/j.issn:0438-1157.2003.06.025 CrossRef Google Scholar LIU W L, LU M X, WANG F, et al. Extraction of textural feature and recognition of coal flotation froth[J]. Journal of Chemical Industry and Engineering, 2003, 54(6): 830−835. doi: 10.3321/j.issn:0438-1157.2003.06.025 CrossRef Google Scholar
[35]	程翠兰. 基于颜色与纹理特征的矿物浮选精选泡沫分类[D]. 长沙: 中南大学, 2011. Google Scholar CHENG C L. Classification of mineral flotation foam based on color and texture characteristics[D]. Changsha: Central South University, 2011. Google Scholar
[36]	唐朝晖, 孙园园, 桂卫华, 等. 基于小波变换的浮选泡沫图像纹理特征提取[J]. 计算机工程, 2011, 37(18): 206−208. doi: 10.3969/j.issn.1000-3428.2011.18.069 CrossRef Google Scholar TANG C H, SUN Y Y, GUI W H, et al. Texture feature extraction of flotation foam image based on wavelet transform[J]. Computer Engineering, 2011, 37(18): 206−208. doi: 10.3969/j.issn.1000-3428.2011.18.069 CrossRef Google Scholar
[37]	朱楚梅. 铝土矿精选泡沫图像纹理特征提取方法研究[D]. 长沙: 中南大学, 2013. Google Scholar ZHU C M. Research on texture feature extraction method of bauxite foam image[D]. Changsha: Central South University, 2013. Google Scholar
[38]	马爱莲, 徐德刚, 谢永芳, 等. 基于复杂网络时空特性的泡沫图像动态纹理特征分析[J]. 化工学报, 2017, 68(3): 1023−1031. Google Scholar MA A L, XU D G, XIE Y F, et al. Analysis of dynamic texture features of floatation froth images based on space−time characteristics of complex networks[J]. CIESC Journal, 2017, 68(3): 1023−1031. Google Scholar
[39]	牟学民, 刘金平, 桂卫华, 等. 基于SIFT特征配准的浮选泡沫移动速度提取与分析[J]. 信息与控制, 2011, 40(4): 525−531. Google Scholar MOU X M, LIU J P, GUI W H, et al. Extraction and analysis of flotation foam movement velocity based on SIFT feature registration[J]. Information and Control, 2011, 40(4): 525−531. Google Scholar
[40]	TANG Z, ZENG S, XIE Y, et al. Real−time froth velocity extraction of zinc flotation based on improved SURF[C]//proceedings of the 2018 Chinese Automation Congress (CAC), F, IEEE. 2018. Google Scholar
[41]	陈良琴, 王卫星. 基于气泡跟踪与相位相关的浮选表面气泡平移运动估计[J]. 四川大学学报(工程科学版), 2016, 48(5): 143−152. Google Scholar CHEN L Q, WANG W X, Estimation of bubble translation motion on flotation surface based on bubble tracking and phase correlation[J]. Journal of Sichuan University (Engineering Science Edition), 2016, 48(5): 143−152. Google Scholar
[42]	卜显忠, 杨怡琳, 宛鹤. 基于浮选泡沫图像预测精矿品位的研究进展[J]. 金属矿山, 2024(2): 25−38. Google Scholar BU X Z, YANG Y L, WAN H. Research progress of concentrate grade prediction based on flotation foam image[J]. Metal Mine, 2024(2): 25−38. Google Scholar
[43]	LI Z M, GUI W H, ZHU J Y, Fault diagnosis method of flotation process based on deep learning and support vector machine[J]. Journal of Central South University, 2019, 26(9): 2504−2515. Google Scholar
[44]	WANG X, SONG C, YANG C, et al. Process working condition recognition based on the fusion of morphological and pixel set features of froth for froth flotation[J]. Minerals Engineering, 2018, 128: 17−26. doi: 10.1016/j.mineng.2018.08.017 CrossRef Google Scholar
[45]	梁秀满, 田童, 刘文涛, 等. 基于泡沫图像特征融合的煤泥浮选工况识别[J]. 计算机仿真, 2021, 38(4): 385−389. doi: 10.3969/j.issn.1006-9348.2021.04.078 CrossRef Google Scholar LIANG X M, TIAN T, LIU W T, et al. Coal slime flotation condition recognition based on foam image feature fusion[J]. Computer Simulation, 2021, 38(4): 385−389. doi: 10.3969/j.issn.1006-9348.2021.04.078 CrossRef Google Scholar
[46]	朱建勇, 黄鑫, 杨辉, 等. 基于稀疏化神经网络的浮选泡沫图像特征选择[J]. 控制与决策, 2021, 36(7): 1627−1636. Google Scholar ZHU J Y, HUANG X, YANG H, et al. Feature selection of flotation froth image based on sparse neural network[J]. Control and Decision, 2021, 36(7): 1627−1636. Google Scholar
[47]	周开军, 阳春华, 牟学民, 等. 基于泡沫特征与LS−SVM的浮选回收率预测[J]. 仪器仪表学报, 2009, 30(6): 1295−1300. doi: 10.3321/j.issn:0254-3087.2009.06.034 CrossRef Google Scholar ZHOU K J, YANG C H, MOU X M, et al. Prediction of flotation recovery rate based on foam characteristics and LS−SVM[J]. Chinese Journal of Scientific Instrument, 2009, 30(6): 1295−1300. doi: 10.3321/j.issn:0254-3087.2009.06.034 CrossRef Google Scholar
[48]	张海洋, 王旭, 王庆凯等. 镍浮选过程智能控制系统开发与应用[J]. 有色金属工程, 2024, 14(2): 77−84. Google Scholar ZHANG H Y, WANG X, WANG Q K, et al. Development and application of intelligent control system for nickel flotation process[J]. Nonferrous Metals Engineering, 2024, 14(2): 77−84. Google Scholar
[49]	阳春华, 任会峰, 许灿辉, 等. 基于稀疏多核最小二乘支持向量机的浮选关键指标软测量[J]. 中国有色金属学报, 2011, 21(12): 3149−3154. Google Scholar YANG C H, REN H F, XU C H, et al. Soft measurement of key flotation indexes based on Sparse multi−core least squares support vector machine[J]. The Chinese Journal of Non−Ferrous Metals, 2011, 21(12): 3149−3154. Google Scholar
[50]	张燕红, 刘俊, 陈冲等. 浮选智能控制系统在某钼矿选厂的应用[J]. 矿山机械, 2021, 49(9): 50−54. doi: 10.3969/j.issn.1001-3954.2021.09.012 CrossRef Google Scholar ZHANG Y H, LIU J, CEHN C, et al. Application of flotation intelligent control system in a molybdenum ore separation plant[J]. Mining Machinery, 2021, 49(9): 50−54. doi: 10.3969/j.issn.1001-3954.2021.09.012 CrossRef Google Scholar
[51]	苏超, 王旭. 浮选流程智能控制系统开发与应用[J]. 铜业工程, 2019(4): 4−9. doi: 10.3969/j.issn.1009-3842.2019.04.003 CrossRef Google Scholar SU C, WANG X. Development and application of flotation process intelligent control system[J]. Copper Engineering, 2019(4): 4−9. doi: 10.3969/j.issn.1009-3842.2019.04.003 CrossRef Google Scholar