Research progress and application of online coal quality and coal quantity analyses
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摘要:
当前，煤矿智能化正在向着中高级阶段迈进，煤炭工业数智化转型迫切需要实时掌握煤质煤量全面信息，因此，开展了煤质煤量全面在线检测技术发展现状及应用进展综述研究。在分析煤质煤量检测技术工业应用需求的基础上，重点阐述以激光诱导击穿光谱法（LIBS）及其与其他光谱联合的多光谱联用技术为代表的光谱学技术的基本原理、优缺点、研究进展与工业应用情况和以图像分析为代表的人工智能煤质煤量检测方法。然后，基于不同技术的工业应用情况进行问题梳理，分析实时煤质煤量在线检测技术在工业应用中的技术局限性，包括：基于技术原理的探测精度问题；由复杂环境因素引用的设备稳定性问题；基于大量数据处理的算法分析问题；煤炭全产业链应用的技术适用与灵活性问题。最后，对未来煤质煤量全面分析及在线检测技术提出4点发展建议：结合地质条件的煤质在线检测技术研究；工业化多光谱联用技术研究；光谱学与图像分析技术联用煤质煤量全面分析技术研究；智能化在煤质煤量实时检测的深入应用研究。智能化煤质煤量全面分析及在线检测装备研发需多学科共同努力，基于煤岩学、光谱学、仪器仪表工程、数据处理和模式识别、人工智能和机器学习等多学科科学技术，建立工业应用场景−煤质煤量参数−实际应用指导数据库，是实现智能化煤质煤量在线检测，掌握和预测全面煤质煤量信息的重要发展方向。
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关键词:
- 煤矿智能化 /
- 煤质 /
- 煤流量 /
- 激光诱导击穿光谱分析法 /
- 图像分析与机器视觉
Abstract:The coal industry urgently needs real-time access to comprehensive information on coal quality and quantity for its digital transformation. Based on analyzing the industrial application requirements for comprehensive analysis of coal quality and quantity, this paper focuses on the technical principles, research status, and industrial application of spectroscopic techniques represented by laser-induced breakdown spectroscopy (LIBS) and multispectral fusion with other spectroscopic techniques. It also discusses the artificial intelligence-based coal quality and quantity detection methods represented by image analysis. Then, based on the industrial application scenarios of different technologies, we need to analyze the technical limitations of real-time coal quality and quantity online detection technology in industrial applications. These limitations include detection accuracy issue based on technical principles, equipment stability issue caused by complex environmental factors, algorithm analysis issue based on large-scale data processing, technical applicability, and flexibility issue in the entire coal industry chain, respectively. Finally, four development suggestions for future comprehensive analysis and online detection technology of coal quality and quantity were proposed, they are research on coal quality online detection technology considering geological conditions, research on industrial-scale multispectral fusion technology, research on comprehensive analysis of coal quality and quantity using spectroscopy and image analysis techniques and in-depth research on the application of intelligent technologies in real-time coal quality and quantity detection, respectively. Coal quality online detection is a complex field that involves multiple disciplines and specialized knowledge. It relies on interdisciplinary scientific and technological fields such as coal petrography, spectroscopy, instrument engineering, data processing, pattern recognition, artificial intelligence, and machine learning. Establishing industrial application scenario-coal quality and coal quantity parameters-actual application guidance databases is an important direction for achieving intelligent coal quality and coal quantity online detection and obtaining comprehensive coal quality and coal quantity information.
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图 10 智能化煤质煤量全面检测构想
Figure 10. Conception of intelligent comprehensive detection of coal quality and quantity
表 1 不同光谱类煤质在线检测技术对比分析
Table 1 Comparable analysis of various online determine technologies of coal quality
技术名称 测量指标 精度 应用场景 优缺点 发展前景 天然γ射线检测法 灰分 较差 洗选厂 优点：非接触式检测，标定过程简单，检测结果不受输送带中钢丝影响，安装简单、维护陈本低；
缺点：对煤中放射性元素要求较高，对探头精度要求高，放射性元素与煤中矿物质相关性较好时才能反映所测煤质水平一般 X射线检测法 灰分、硫分 一般 输送带原煤、快速装车、燃煤电厂、洗选厂 优点：非接触式检测，安装简单、维护量小，测量范围广，煤种适用性强、现场环境适应性强；
缺点：只适用于检测原子序数＞11的元素，测量精度一般，对被测煤流粒度与厚度均有要求一般 近红外光谱法 水分、硫分、氢、挥发分、发热量 较高 燃煤电厂、
洗选厂优点：快速无损、检测信息量大、精度高，分辨率高；
缺点：被检测样品中某项成分含量大于0.1%；对探测设备要求较高；煤的近红外光谱信息繁杂，需提取相关信息，构建分析模型较好 拉曼光谱法 水分、硫分、氢、发热量 较高 燃煤电厂 优点：高效、快速、原位检测，拉曼光谱特征具有唯一性；
缺点：拉曼光谱仪成本较高、设备安装与维护相对困难，煤的拉曼散射信号弱，灵敏度不高，容易受环境因素影响一般 激光诱导击穿
光谱分析法C、H、O、N、Si、Al、Na、K、Mg、Ca、Fe、Li 较低 原煤煤流、洗选厂、发电厂、
煤化工厂优点：制样简单、快速、原位、远程、全元素同步分析
缺点：存在基体效应、光谱信号不确定度，定量分析精度低好 联用
技术LIBS-Raman 水分、灰分、挥发分、固定碳、发热量等 较高 原煤煤流、洗选厂、发电厂、
煤化工厂优点：快速、原位、远程、全元素同步分析，多技术联用实现多参数测量与准确性验证，可提高设备适用性
缺点：考虑器件功能融合还要考虑整体载荷小型化、一体化等
问题好 LIBS-XRF LIBS-NIRS 
下载: 导出CSV
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