激光诱导击穿光谱煤质在线分析技术现状与展望  

Current situation and prospect of online coal analysis by laser-induced breakdown spectroscopy

作　　者：侯宗余[1,2,3] 宋惟然 宋玉洲 顾炜伦 刘家岑 王哲 HOU Zongyu;SONG Weiran;SONG Yuzhou;GU Weilun;LIU Jiacen;WANG Zhe(State Key Laboratory of Power System Operation and Control,Department of Energy and Power Engineering,Tsinghua University,Beijing 100084,China;International Joint Laboratory on Low Carbon Clean Energy Innovation,Tsinghua University,Beijing 100084,China;Shanxi Research Institute for Clean Energy,Tsinghua University,Taiyuan 030032,China)

机构地区：[1]清华大学能源与动力工程系新型电力系统运行与控制全国重点实验室,北京100084 [2]低碳清洁能源创新国际联合实验室,北京100084 [3]清华大学山西清洁能源研究院,山西太原030032

出　　处：《煤质技术》2023年第1期1-12,共12页Coal Quality Technology

基　　金：国家自然科学基金资助项目(51906124)。

摘　　要：煤质在线分析技术可为洗选煤、混配煤、燃烧优化等提供快速煤质特性数据,因而其为实现煤炭清洁高效利用的关键支撑技术。激光诱导击穿光谱(LIBS)具有全元素分析、原位实时测量、安全无辐射等优势,成为最具潜力的煤质在线分析技术之一。简要介绍LIBS技术的基本原理,阐述提高LIBS煤质分析定量化性能的方法及提升长期稳定性需注意的问题,剖析LIBS煤质在线分析常用测量与布置方式等并展望LIBS煤质在线分析的发展趋势。研究表明:LIBS煤质在线分析技术的可行性已得以验证并研制煤块测量、煤粉测量、压片测量等多种测量方式的设备,目前压片式测量仍是LIBS煤质在线分析较合适的方式;在基础研究方面需深入研究激光、煤、等离子体和环境气体之间的相互作用机制,进一步开发提高LIBS煤质分析可重复性并降低基体效应影响的技术方法;在定量化模型方面,不仅要结合大数据、人工智能等先进的机器学习算法,也应考虑LIBS煤质分析的物理背景并将其融入模型中;采用LIBS与微波、XRF、拉曼等其他技术相结合以提高硫和微量元素的定量性能也将是未来重要的研究方向;在工业应用中还需重点考虑测量代表性、设备的长期稳定性和煤种适应性等因素,从而实现长期稳定测量并降低维护工作量。Online coal analysis technology can quickly provide coal property data for coal washing and separation,coal blending,and combustion optimization.It is a key technology for clean and efficient utilization of coal.With the advantages of full-element analysis,in-situ real-time measurement,and safety,laser-induced breakdown spectroscopy(LIBS)has become one of the most potential coal online analysis techniques.This paper briefly introduces the basic principles of LIBS,and then expounds the methods of improving LIBS quantitative performance and long-term repeatability for coal analysis.The measurement layout mode is also discussed,and finally the trend of LIBS online coal analysis is prospected.The research shows that the feasibility of LIBS coal online analysis has been verified.For sample preparation,coal samples can be measured in the form of lumps,powders,and pellets.Pellets are still a more suitable method for online analysis of LIBS coal.For mechanism research,the interaction between laser,coal,plasma and ambient gas needs to be studied to further improve the repeatability and weaken the influence of matrix effect.For quantitative models,not only advanced machine learning algorithms should be combined,but also the physical process of LIBS coal analysis should be considered.Meanwhile,the combination with microwave,XRF,Raman and other technologies to improve the quantitative performance of sulfur and trace elements is also important.For industrial applications,other factors such as measurement representativeness,long-term stability of equipment and coal adaptability need to be carefully considered to achieve long-term stable measurement and reduce maintenance workload.

关 键 词：激光诱导击穿光谱 煤质在线分析技术 压片式测量 基体效应 定量化模型 机器学习算法 定量性能 

分 类 号：TQ533.6[化学工程—煤化学工程]