Research progress and application of online coal quality and coal quantity analyses

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.