Machine learning-assisted study on modulating aluminosilicate framework of high-silica coal ash via calcium extracted from waste oyster shells
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摘要
This study addresses the challenge of excessively high ash fusion temperatures (AFTs) in high-silicon-aluminum coal from Guizhou, which limits its application in entrained-flow gasification. Using Jinsha (JS) and Nayong (NY) coals, we investigate waste oyster shell (OS) as a calcium-based flux and its regulation mechanism. Analyses including ash fusion tests, SEM, XRF, XRD, and FactSage simulations were performed to examine melting behavior, mineral evolution, and microstructural changes upon oyster shell ash (OSA) addition. A machine learning model was also built to quantitatively identify key factors influencing flow temperature (FT). Results show that OSA addition effectively reduces AFTs of both JS ash (JSA) and NY ash (NYA). At 17.5 wt.% OSA, FTs of JSA and NYA decrease significantly to 1370 °C from >1500 °C and 1468 °C, respectively. Mechanistically, the fluxing effect on JSA is attributed to Ca²⁺-induced disruption of the aluminosilicate network. For NYA, effective FT reduction occurs only above a 10.5 wt.% OSA threshold, due to multicomponent eutectic formation. Machine learning identifies the Ca/Si ratio as the most dominant factor negatively correlated with FT, offering data-driven support for experimental observations. This study provides an economically viable fluxing option for clean utilization of high-silicon-aluminum coal and a theoretical basis for waste OS resource recovery.
关键词
Oyster shells; High-AFTs coal; Aluminosilicate; Calcium-based flux
报告人
Mei-Ling Xu
Professor Heze University

稿件作者
Yu Wang Yili Normal University ;Heze University
Mei-Ling Xu Heze University
Ruozhan Pan Heze University
Guosheng Li Heze University
Xing Fan Shandong University of Science and Technology;Yili Normal University
Yuwen Huang Heze University
Fenghai Li Heze University
Xian-Yong Wei Yili Normal University
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重要日期
  • 会议日期

    11月20日

    2026

    11月24日

    2026

  • 09月30日 2026

    初稿截稿日期

主办单位
China University of Mining and Technology
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