基于水传热和红外热成像的煤矸识别方法

程刚; 陈杰; 潘泽烨; 魏溢凡; 陈森森

doi:10.13272/j.issn.1671-251x.2023050056

基于水传热和红外热成像的煤矸识别方法

doi: 10.13272/j.issn.1671-251x.2023050056

程刚^{1, 2,},
陈杰^{1, 2, ,},
潘泽烨²,
魏溢凡²,
陈森森²

1.
安徽理工大学深部煤矿采动响应与灾害防控国家重点实验室，安徽淮南　232001
2.
安徽理工大学机械工程学院，安徽淮南　232001

基金项目: 安徽省高校协同创新项目（GXXT-2021-076）；煤炭安全精准开采国家地方联合工程研究中心研究项目（EC2021010）。

详细信息

作者简介:
程刚（1986—），男，安徽桐城人，副教授，博士，研究方向为煤矿智能开采，E-mail：chgmech@mail.ustc.edu.cn

通讯作者:
陈杰（1997—），男，安徽铜陵人，硕士研究生，研究方向为矿山机电工程，E-mail:ChenJie7197@163.com。

中图分类号: TD67
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-17
- 修回日期: 2024-01-18
- 网络出版日期: 2024-01-31

Coal gangue recognition method based on water heat transfer and infrared thermal imaging

CHENG Gang^{1, 2
,},
CHEN Jie^{1, 2
, ,},
PAN Zeye²,
WEI Yifan²,
CHEN Sensen²

1.
State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China
2.
School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China

摘要

摘要: 基于可见光图像的煤矸识别方法准确率不高、识别速度慢；基于高能射线透射的煤矸识别方法具有很大辐射导致较少应用。红外热成像具有穿透性强、不受光线影响等优点，但煤和矸石的表面温度在室温下相对接近，导致煤和矸石在红外热图像中没有明显差异，难以获得较好的识别效果。针对上述问题，提出了一种基于水传热和红外热成像的煤矸识别方法。在不同水温（18，21，24，27，30 ℃）条件下进行煤和矸石红外热成像实验，通过煤和矸石红外热图像和温度变化之间的差异来区分煤和矸石。实验结果表明：不同水温下煤和矸石红外热图像不同，当水温低于环境温度时，煤和矸石红外热图像之间的差异较大；在相同水温条件下，煤和矸石红外热图像之间的差异随着时间增加逐渐增大；煤和矸石表面温度变化均随水温升高和时间增加呈增大趋势，但矸石表面温度变化大于煤表面温度变化；当水温为18 ℃、时间为180 s时，煤和矸石红外热图像之间差异和温差均达到最大。这说明低温的水可作为一种传热介质，更有利于使煤和矸石之间产生较大的温差，从而实现煤和矸石红外热图像准确、快速识别。
- 煤矸识别 /
- 水传热 /
- 红外热成像 /
- 红外热图像 /
- 温差
Abstract: The coal gangue recognition method based on visible light images has low accuracy and slow recognition speed. The coal gangue recognition method based on high-energy ray transmission has significant radiation, resulting in limited application. The infrared thermal imaging has advantages such as strong penetration and no influence from light. But the surface temperature of coal and gangue is relatively close at room temperature, resulting in no significant difference between coal and gangue in infrared thermal images, making it difficult to achieve good recognition results. In order to solve the above problems, a coal gangue recognition method based on water heat transfer and infrared thermal imaging is proposed. The method conducts infrared thermal imaging experiments on coal and gangue under different water temperatures (18, 21, 24, 27, 30 ℃). The method distinguishes between coal and gangue based on the differences in infrared thermal images and temperature changes. The experimental results show that the infrared thermal images of coal and gangue are different at different water temperatures. When the water temperature is lower than the ambient temperature, there is a significant difference between the infrared thermal images of coal and gangue. Under the same water temperature conditions, the difference between the infrared thermal images of coal and gangue gradually increases with time. The surface temperature changes of coal and gangue both show an increasing trend with the increase of water temperature and time. But the surface temperature changes of gangue are greater than those of coal. When the water temperature is 18℃ and the time is 180 s, the difference and temperature difference between the infrared thermal images of coal and gangue reach their maximum. This indicates that low-temperature water can serve as a heat transfer medium, which is more conducive to creating a large temperature difference between coal and gangue. The accurate and rapid recognition of coal and gangue infrared thermal images can be achieved.
- coal gangue recognition /
- water heat transfer /
- infrared thermal imaging /
- infrared thermal image /
- temperature difference

HTML全文

图 1 红外热成像原理

Figure 1. Principle of infrared thermal imaging

下载: 全尺寸图片幻灯片

图 2 煤矸红外热成像实验系统

Figure 2. Experimental system of coal and gangue infrared thermal imaging

下载: 全尺寸图片幻灯片

图 3 不同水温下不同时刻的煤和矸石红外热图像

Figure 3. Infrared thermal images of coal and gangue under different water temperatures and time

下载: 全尺寸图片幻灯片

图 4 不同水温下煤和矸石表面温度变化曲线

Figure 4. Surface temperature variation curves of coal and gangue under different water temperatures

下载: 全尺寸图片幻灯片

图 5 煤和矸石表面温度变化与水温和时间的关系

Figure 5. Surface temperature variation of coal and gangue in relation to water temperature and time

下载: 全尺寸图片幻灯片

图 6 不同水温下煤和矸石表面温度变化的均值和方差

Figure 6. Mean and variance of surface temperature variation of coal and gangue under different water temperatures

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图 7 不同水温下煤和矸石表面温差

Figure 7. Surface temperature difference between coal and gangue under different water temperatures

下载: 全尺寸图片幻灯片

参考文献(21)

[1]	袁亮. 我国煤炭主体能源安全高质量发展的理论技术思考[J]. 中国科学院院刊,2023,38(1):11-22. YUAN Liang. Theory and technology considerations on high-quality development of coal main energy security in China[J]. Bulletin of Chinese Academy of Sciences,2023,38(1):11-22.
[2]	王国法,富佳兴,孟令宇. 煤矿智能化创新团队建设与关键技术研发进展[J]. 工矿自动化,2022,48(12):1-15. WANG Guofa,FU Jiaxing,MENG Lingyu. Development of innovation team construction and key technology research in coal mine intelligence[J]. Journal of Mine Automation,2022,48(12):1-15.
[3]	袁亮,张通,张庆贺,等. 双碳目标下废弃矿井绿色低碳多能互补体系建设思考[J]. 煤炭学报,2022,47(6):2131-2139. YUAN Liang,ZHANG Tong,ZHANG Qinghe,et al. Construction of green,low-carbon and multi-energy complementary system for abandoned mines under global carbon neutrality[J]. Journal of China Coal Society,2022,47(6):2131-2139.
[4]	YANG Mei,GUO Zhixing,DENG Yinsheng,et al. Preparation of CaO-Al₂O₃-SiO₂ glass ceramics from coal gangue[J]. International Journal of Mineral Processing,2012,102:112-115.
[5]	XUE Bo,ZHANG Yong,LI Jian,et al. A review of coal gangue identification research-application to China's top coal release process[J]. Environmental Science and Pollution Research,2023,30(6):14091-14103.
[6]	LI Deyong,WANG Guofa,ZHANG Yong,et al. Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3[J]. IET Image Processing,2022,16(1):134-144. doi: 10.1049/ipr2.12339
[7]	王家臣,李良晖,杨胜利. 不同照度下煤矸图像灰度及纹理特征提取的实验研究[J]. 煤炭学报,2018,43(11):3051-3061. WANG Jiachen,LI Lianghui,YANG Shengli. Experimental study on gray and texture features extraction of coal and gangue image under different illuminance[J]. Journal of China Coal Society,2018,43(11):3051-3061.
[8]	薛光辉,李秀莹,钱孝玲,等. 基于随机森林的综放工作面煤矸图像识别[J]. 工矿自动化,2020,46(5):57-62. XUE Guanghui,LI Xiuying,QIAN Xiaoling,et al. Coal-gangue image recognition in fully-mechanized caving face based on random forest[J]. Industry and Mine Automation,2020,46(5):57-62.
[9]	杨慧刚,乔志敏. 基于X射线和机器视觉的煤与矸石分选系统设计[J]. 工矿自动化,2017,43(3):85-89. YANG Huigang,QIAO Zhimin. Design of separation system of coal and gangue based on X-ray and machine vision[J]. Industry and Mine Automation,2017,43(3):85-89.
[10]	李曼,段雍,曹现刚,等. 煤矸分选机器人图像识别方法和系统[J]. 煤炭学报,2020,45(10):3636-3644. LI Man,DUAN Yong,CAO Xiangang,et al. Image identification method and system for coal and gangue sorting robot[J]. Journal of China Coal Society,2020,45(10):3636-3644.
[11]	张释如,黄综浏,张袁浩,等. 基于改进YOLOv5的煤矸识别研究[J]. 工矿自动化,2022,48(11):39-44. ZHANG Shiru,HUANG Zongliu,ZHANG Yuanhao,et al. Coal and gangue recognition research based on improved YOLOv5[J]. Journal of Mine Automation,2022,48(11):39-44.
[12]	郭永存,何磊,刘普壮,等. 煤矸双能X射线图像多维度分析识别方法[J]. 煤炭学报,2021,46(1):300-309. GUO Yongcun,HE Lei,LIU Puzhuang,et al. Multi-dimensional analysis and recognition method of coal and gangue dual-energy X-ray images[J]. Journal of China Coal Society,2021,46(1):300-309.
[13]	王淑,冷航,陈彦伶,等. 红外热成像技术在孔隙尺度下多孔介质相变过程表征中的应用与优化研究[J]. 红外技术,2022,44(3):294-302. doi: 10.11846/j.issn.1001-8891.2022.3.hwjs202203013 WANG Shu,LENG Hang,CHEN Yanling,et al. Application of infrared thermography for characterizing phase change process of porous media at pore scale[J]. Infrared Technology,2022,44(3):294-302. doi: 10.11846/j.issn.1001-8891.2022.3.hwjs202203013
[14]	QU Zhi,JIANG Peng,ZHANG Weixu. Development and application of infrared thermography non-destructive testing techniques[J]. Sensors,2020,20(14):3851-3876. doi: 10.3390/s20143851
[15]	PAN Dong,JIANG Zhaohui,CHEN Zhipeng,et al. Compensation method for molten iron temperature measurement based on heterogeneous features of infrared thermal images[J]. IEEE Transactions on Industrial Informatics,2020,16(11):7056-7066. doi: 10.1109/TII.2020.2972332
[16]	卜迟武,刘涛,李锐,等. 光伏电池缺陷红外热成像检测与图像序列处理[J]. 光学学报,2022,42(7):118-124. BU Chiwu,LIU Tao,LI Rui,et al. Infrared thermography detection and images sequence processing for defects in photovoltaic cells[J]. Acta Optica Sinica,2022,42(7):118-124.
[17]	程继杰,刘毅,李小伟. 基于热红外图像的煤矿冲击地压和煤与瓦斯突出感知报警方法[J]. 煤炭学报,2023,48(5):2236-2248. CHENG Jijie,LIU Yi,LI Xiaowei. Coal mine rock burst and coal and gas outburst perception alarm method based on thermal infrared image[J]. Journal of China Coal Society,2023,48(5):2236-2248.
[18]	赵小虎,车亭雨,叶圣,等. 煤体红外热像异常区域分割方法[J]. 工矿自动化,2022,48(9):92-99. ZHAO Xiaohu,CHE Tingyu,YE Sheng,et al. Segmentation method of the abnormal area of coal infrared thermal image[J]. Journal of Mine Automation,2022,48(9):92-99.
[19]	张志强,王萍,于旭东,等. 高精度红外热成像测温技术研究[J]. 仪器仪表学报,2020,41(5):10-18. ZHANG Zhiqiang,WANG Ping,YU Xudong,et al. Study on high accuracy temperature measurement technology of infrared thermal imager[J]. Chinese Journal of Scientific Instrument,2020,41(5):10-18.
[20]	孙继平,李迎春. 矿井大气中红外辐射传输特性[J]. 煤炭学报,2005,30(6):788-791. doi: 10.3321/j.issn:0253-9993.2005.06.024 SUN Jiping,LI Yingchun. Transmission characteristic of infrared radiation through mine atmosphere[J]. Journal of China Coal Society,2005,30(6):788-791. doi: 10.3321/j.issn:0253-9993.2005.06.024
[21]	国家安全生产监督管理总局. 煤矿安全规程[M]. 北京:煤炭工业出版社,2022. State Administration of Work Safety. Coal mine safety regulations[M]. Beijing:Coal Industry Press,2022.