| Citation: | GENG Yanbing, WANG Zhangguo. A fast detection method for slime water flocculation and sedimentation rate based on image grayscale recognition[J]. Journal of Mine Automation，2023，49（12）：87-93. doi: 10.13272/j.issn.1671-251x.2023050083
|
At present, there is a lack of effective online detection methods for important parameters such as mineral composition that affect the flocculation and sedimentation effect of slime water. There are also lagging issues in the turbidity and interface of the overflow of the concentration tank, which limits the development of intelligent dosing for slime water in coal preparation plants. In order to solve the above problems, a fast detection method for slime water flocculation and sedimentation rate based on image grayscale recognition is proposed. Using a CCD camera to collect images of the sedimentation process of slime water online, and using the mean filtering method for noise reduction, the average grayscale and average grayscale change rate of the image are calculated. The sedimentation rate is obtained by using the relationship between the sedimentation rate and the average grayscale change rate. The method extracts feature values such as grayscale, energy, contrast, variance, and cross-correlation from images through flocculation sedimentation experiments for analysis and verification. The analysis results show the following points. ① Among the five image features, the change in grayscale mean conforms to the variation law of sedimentation rate during the sedimentation process of slime water batches. There are buffer zones, linear zones, and stable zones, and the variation features can be obtained within 30 seconds. ② There is a good linear correlation between the average grayscale change rate and sedimentation rate. When the concentration of slime water is 20 g/L, the linear correlation coefficient between the average grayscale change rate of the image and sedimentation rate under different flocculant addition amounts is 0.977 2. Under the conditions of slime water concentration of 5-25 g/L and flocculant addition amounts of 0.1-0.2 kg/t, the linear correlation coefficient between the two is 0.944 1. ③ The average grayscale change rate can adapt to the changes in the flocculation and sedimentation state of slime water within a large range. The average grayscale change rate can be used to quickly detect the flocculation and sedimentation rate of slime water and serve as the basis for intelligent adjustment of slime water dosing.
| [1] |
党瑞德,张月飞,林喆. 哈尔乌素选煤厂煤泥水絮凝影响因素研究[J]. 煤炭工程,2021,53(增刊1):103-107.
DANG Ruide,ZHANG Yuefei,LIN Zhe. Influencing factors of slime water flocculation in Harwusu Coal Preparation Plant[J]. Coal Engineering,2021,53(S1):103-107.
|
| [2] |
NI Chao,BU Xiangning,XIA Wencheng,et al. Observing slime-coating of fine minerals on the lump coal surface using particle vision and measurement[J]. Powder Technology,2018,339:434-439. doi: 10.1016/j.powtec.2018.08.034
|
| [3] |
宋帅,樊玉萍,马晓敏,等. 煤泥水中煤与不同矿物相互作用的模拟研究[J]. 矿产综合利用,2020(1):168-172,102. doi: 10.3969/j.issn.1000-6532.2020.01.034
SONG Shuai,FAN Yuping,MA Xiaomin,et al. Simulation study on interaction between coal and different minerals in coal slurry[J]. Multipurpose Utilization of Mineral Resources,2020(1):168-172,102. doi: 10.3969/j.issn.1000-6532.2020.01.034
|
| [4] |
周青松. 浅析煤泥水处理对选煤工艺的影响[J]. 矿业装备,2022(5):18-20.
ZHOU Qingsong. Analysis of the impact of coal slurry water treatment on coal preparation process[J]. Mining Equipment,2022(5):18-20.
|
| [5] |
韩峰,孔令超,车立润,等. 煤泥水沉降检测控制系统的研究与应用[J]. 煤炭加工与综合利用,2023(1):77-78. doi: 10.16200/j.cnki.11-2627/td.2023.01.019
HAN Feng,KONG Lingchao,CHE Lirun,et al. Research and application of coal slurry settlement detection and control system[J]. Coal Processing & Comprehensive Utilization,2023(1):77-78. doi: 10.16200/j.cnki.11-2627/td.2023.01.019
|
| [6] |
毛箫瑀,刘令云. 煤泥水处理智能控制研究现状[J]. 选煤技术,2022,50(2):78-85.
MAO Xiaoyu,LIU Lingyun. Present status of the research work on intelligent control in slime water treatment[J]. Coal Preparation Technology,2022,50(2):78-85.
|
| [7] |
窦红庆,高晓茜,张新明. 选煤厂智能加药设计与应用[J]. 洁净煤技术,2023,29(增刊1):127-130.
DOU Hongqing,GAO Xiaoxi,ZHANG Xinming. Design and application of intelligent dosing in coal preparation plant[J]. Clean Coal Technology,2023,29(S1):127-130.
|
| [8] |
杨津灵,杨洁明,魏晋宏,等. 基于灰色预测−模糊控制的絮凝剂自动添加系统设计[J]. 太原理工大学学报,2012,43(5):606-609. doi: 10.3969/j.issn.1007-9432.2012.05.018
YANG Jinling,YANG Jieming,WEI Jinhong,et al. Automatic flocculant adding system based on grey prediction fuzzy control[J]. Journal of Taiyuan University of Technology,2012,43(5):606-609. doi: 10.3969/j.issn.1007-9432.2012.05.018
|
| [9] |
邓建军,张孝逐,林喆,等. 基于光电测量的煤泥水自动加药系统研究[J]. 洁净煤技术,2017,23(2):92-97,102. doi: 10.13226/j.issn.1006-6772.2017.02.018
DENG Jianjun,ZHANG Xiaozhu,LIN Zhe,et al. Study on the automatic dosing system of coal slurry based on photoelectric measurement[J]. Clean Coal Technology,2017,23(2):92-97,102. doi: 10.13226/j.issn.1006-6772.2017.02.018
|
| [10] |
张明青,刘颀,宋灿灿. 从黏土行为视角认识煤泥水沉降性能[J]. 选煤技术,2021(1):44-49. doi: 10.16447/j.cnki.cpt.2021.01.005
ZHANG Mingqing,LIU Qi,SONG Cancan. Exploration of coal slurry sedimentation characteristics from the perspective of clay behavior[J]. Coal Preparation Technology,2021(1):44-49. doi: 10.16447/j.cnki.cpt.2021.01.005
|
| [11] |
折小江,刘江,王兰豪. AI视频图像分析在选煤厂智能化中的应用现状与发展趋势[J]. 工矿自动化,2022,48(11):45-53,109.
SHE Xiaojiang,LIU Jiang,WANG Lanhao. Application status and prospect of AI video image analysis in intelligent coal preparation plant[J]. Journal of Mine Automation,2022,48(11):45-53,109.
|
| [12] |
种亚岗,石晓军,陈锋,等. 机器视觉技术在选煤厂应用的研究现状和进展[J]. 矿山机械,2017,45(7):57-59. doi: 10.16816/j.cnki.ksjx.2017.07.014
Chong Yagang,SHI Xiaojun,CHEN Feng,et al. Research status and development of application of machine vision technology in coal washery[J]. Mining & Processing Equipment,2017,45(7):57-59. doi: 10.16816/j.cnki.ksjx.2017.07.014
|
| [13] |
薛旭升,杨星云,齐广浩,等. 煤矿带式输送机分拣机器人异物识别与定位系统设计[J]. 工矿自动化,2022,48(12):33-41.
XUE Xusheng,YANG Xingyun,QI Guanghao,et al. Design of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor[J]. Journal of Mine Automation,2022,48(12):33-41.
|
| [14] |
周德炀,张立忠,景治,等. 基于机器视觉的煤质快速分析法及其应用[J]. 煤炭加工与综合利用,2020(8):78-80. doi: 10.16200/j.cnki.11-2627/td.2020.08.022
ZHOU Deyang,ZHANG Lizhong,JING Zhi,et al. The quick analysis method and its application of coal quality based on machine vision[J]. Coal Processing & Comprehensive Utilization,2020(8):78-80. doi: 10.16200/j.cnki.11-2627/td.2020.08.022
|
| [15] |
丁泽海. 基于机器视觉的高硫煤煤质分析研究[D]. 徐州:中国矿业大学,2018.
DING Zehai. Analysis of high sulfur coal property based on machine vision[D]. Xuzhou:China University of Mining and Technology,2018.
|
| [16] |
赵瑞,陆博. 在线图像分析系统在浮选优化控制中的应用[J]. 中国矿业,2019,28(增刊2):214-218.
ZHAO Rui,LU Bo. Application of on-line image analysis system in flotation optimization control[J]. China Mining Magazine,2019,28(S2):214-218.
|
| [17] |
李强. 基于语义理解的图像检索研究[D]. 天津:天津大学,2016.
LI Qiang. A study on image retrieval based on semantic understanding[D]. Tianjin:Tianjin University,2016.
|
| [18] |
汪岩,李自强. 基于AI图像处理的煤矸石特征提取及分类方法[J]. 煤炭技术,2023,42(11):231-233.
WANG Yan,LI Ziqiang. Feature extraction and classification method of coal gangue based on AI image processing[J]. Coal Technology,2023,42(11):231-233.
|
| [19] |
LIN Zhe,LI Panting,HOU Dou,et al. Aggregation mechanism of particles:effect of Ca2+ and polyacrylamide on coagulation and flocculation of coal slime water containing illite[J]. Minerals,2017,7(2). DOI: 10.3390/min7020030.
|
| [20] |
林喆,杨超,沈正义,等. 高泥化煤泥水的性质及其沉降特性[J]. 煤炭学报,2010,35(2):312-315. doi: 10.13225/j.cnki.jccs.2010.02.008
LIN Zhe,YANG Chao,SHEN Zhengyi,et al. The properties and sedimentation characteristics of extremely sliming coal slime water[J]. Journal of China Coal Society,2010,35(2):312-315. doi: 10.13225/j.cnki.jccs.2010.02.008
|
| [21] |
LIN Zhe,WANG Quanwu,WANG Tuqing,et al. Dynamic floc characteristics of flocculated coal slime water under different agent conditions using particle vision and measurement[J]. Water Environment Research,2020,92(5):706-712. doi: 10.1002/wer.1261
|
Figures(7)
Supported by:
Beijing Renhe Information Technology Co. Ltd.
Visits：
DownLoad:
