摘要
基于“液体介入+红外检测”技术设计了不同浓度表面活性剂辅助下的煤矸识别实验,从煤矸温变规律、煤矸温差规律、混矸率及识别准确率计算三方面分析了活性剂辅助对煤矸石识别效果的影响。实验结果表明,表面活性剂可极大增强煤矸温变效应,但不同浓度的活性剂扩大煤矸温差的程度不同;与清水对照组相比,0.05%浓度的DTAB溶液具有最佳的煤矸短时温差提升效果,可大幅提高基于红外图像的煤矸识别准确率。该实验设计基于智能放煤领域学科前沿成果,涉及采矿、计算机、化学等多个学科的知识交叉,能够提升学生综合运用跨学科知识解决实际问题的能力。
[Objective]The fast,accurate identification of coal and gangue is one of the essential technologies of intelligent mining,which is critical to promoting green intelligent mining of thick coal seams in China.Because the surface colors of gangue and coal are remarkably close,identification errors often arise when visible light image recognition technology is used.However,traditional infrared recognition technology cannot effectively distinguish gangue from coal owing to the small temperature difference between them.Therefore,a modern technology of“liquid intervention+infrared detection”is suggested to identify coal gangue by spraying specific types of liquid on the surface of coal and gangue.[Methods]The different temperature drop occurs on the surface of coal and gangue,that is,the area of coal and gangue in the infrared image is increased by active intervention.Then,an infrared thermal imager and an image recognition algorithm are used to distinguish coal and gangue and calculate the gangue mixing rate.Considering that surfactants can change the surface properties of coal and gangue and thus expand the temperature difference between coal and gangue,experiments on coal gangue identification with the assistance of different concentrations of surfactants based on the new technology of“liquid intervention+infrared detection”are performed.The influence of surfactants on the coal/gangue recognition effect is investigated from three aspects:coal temperature variation law,coal–gangue temperature difference law,mixed rate,and identification accuracy.[Results]The experimental results reveal the following:1)Surfactants can immensely improve the temperature variation of coal and gangue,but the extent to which diverse concentrations of surfactants enlarge the temperature variance of coal waste varies.2)DATB surfactant has the best effect on lowering coal temperature,whereas CTAB surfactant has the best influence on lowering gangue temperature.The cooling effect of anionic surfactants SDBS and SDS on coal waste is connected with their concentration,but not directly proportional.3)Compared with the clean water controlled group,the DATB solution with 0.05%concentration has the greatest short-term temperature difference enhancement on coal and gangue.At 4 s after the intervention,the temperature variance between coal and gangue increases by about 73%compared with that of clean water,and the recognition accuracy rate reaches 97.71%,which can substantially enhance the recognition accuracy rate of coal gangue based on infrared images.4)Because the temperature variance between coal and gangue is decided by the temperature drop of coal and gangue simultaneously,expanding their temperature difference needs to enhance the temperature drop of coal and weaken that of gangue.Hence,the performance of coal–gangue identification aided by surfactants can be further strengthened by merging various surfactants to fully develop the characteristics and synergistic effect of different surfactants.[Conclusions]The experimental design is based on the innovative achievements in the field of intelligent coal drawing.The experimental process entails the intersection of multiple disciplines,such as mining engineering,computer science,and chemistry,which advances the students’ability to apply interdisciplinary knowledge comprehensively to solve practical problems and is crucial for cultivating high-quality intelligent mining talents.
作者
张锦旺
王逢辰
ZHANG Jinwang;WANG Fengchen(School of Energy and Mining Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;National Experimental Teaching Demonstration Center for Safe Coal Mining and Geological Guarantee,Beijing 100083,China)
出处
《实验技术与管理》
CAS
北大核心
2024年第5期188-194,共7页
Experimental Technology and Management
基金
北京市自然科学基金面上项目(2232059)
国家自然科学基金面上项目(52374148)
北京高等教育本科生教学改革创新项目(201911413005)
中国矿业大学(北京)本科教育教学改革与研究项目(J231104)
中央高校基本科研业务费专项(2023JCCXNY04)。
关键词
智能采矿
煤矸识别实验
表面活性剂
红外图像
intelligent mining
coal/gangue recognition experiment
surfactant
infrared image