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<p>This paper surveys the state-of-the-art and emerging directions of on-line coal-ash measurement, a critical parameter for efficient, clean coal utilization. First, active techniques—dual-energy γ-ray transmission, neutron-activation analysis, X-ray penetration and X-ray fluorescence—are examined: they deliver high speed and precision, yet entail high capital cost and rigorous management of radioactive sources. Second, passive approaches—laser-induced breakdown spectroscopy, near-infrared spectroscopy, natural γ-ray and Raman methods—avoid radioactivity but exhibit accuracy strongly modulated by variable coal matrices. The review then synthesizes foreseeable trends: invention of next-generation sensors; hybrid or optimized versions of existing hardware; deep embedding of AI, machine-learning and digital-twin platforms; and large-model-driven customization for site-specific seams. Standardization of protocols, data formats and calibration chains is highlighted as prerequisite for industry-wide adoption. Finally, the paper argues that future ash monitors will be built around four converging pillars—intelligence, digitization, standardization and customization—leveraging artificial-intelligence algorithms and big-data governance to deliver accurate, rapid and environmentally sustainable coal-quality control across mining, preparation and conversion processes.</p>

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Advances in Online Coal Ash Analysis: A Comprehensive Review of Detection Technologies and Future Intelligent Trends

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Xiaosong Bai,
  • Silong Zhou,
  • Jiahua Cui,
  • Dengyang Su,
  • Shan Li,
  • Gen Huang,
  • Jiushuai Deng,
  • Yijun Cao,
  • Junguo Li,
  • Hongxiang Xu

摘要

This paper surveys the state-of-the-art and emerging directions of on-line coal-ash measurement, a critical parameter for efficient, clean coal utilization. First, active techniques—dual-energy γ-ray transmission, neutron-activation analysis, X-ray penetration and X-ray fluorescence—are examined: they deliver high speed and precision, yet entail high capital cost and rigorous management of radioactive sources. Second, passive approaches—laser-induced breakdown spectroscopy, near-infrared spectroscopy, natural γ-ray and Raman methods—avoid radioactivity but exhibit accuracy strongly modulated by variable coal matrices. The review then synthesizes foreseeable trends: invention of next-generation sensors; hybrid or optimized versions of existing hardware; deep embedding of AI, machine-learning and digital-twin platforms; and large-model-driven customization for site-specific seams. Standardization of protocols, data formats and calibration chains is highlighted as prerequisite for industry-wide adoption. Finally, the paper argues that future ash monitors will be built around four converging pillars—intelligence, digitization, standardization and customization—leveraging artificial-intelligence algorithms and big-data governance to deliver accurate, rapid and environmentally sustainable coal-quality control across mining, preparation and conversion processes.