Abstract In entrained-flow gasification, solid fuel is brought in contact with oxygen and steam, yielding slagging conditions at temperatures of 1250–1800 °C. The process temperature cannot be chosen freely but is determined by the melting behavior of the coal ash. By blending different coals and fluxing agents, the ash fusion temperature can be lowered allowing operation at a lower reactor temperature and savings in oxygen. Since ash fusion behavior is not measurable online, it can be beneficial to use a quickly measured coal ash composition and estimate the ash fusion behavior instantly. In this work, >300 different coal samples from all over the world were investigated. This includes ash compositions determined from X-ray fluorescence (XRF) analysis and standard ash fusion behavior under reducing and oxidizing conditions. In a systematic approach, the ash components were limited to the most significant ones to optimize calculation time. The software ChemApp was used to calculate thermodynamic equilibrium based on FToxid and FactPS databases. The obtained results involve the temperatures at which 10 to 100% of the ash melt are liquid slag calculated in 10%-pts steps. According to the applied atmosphere, the obtained results have been statistically correlated to the experimentally determined fusion temperatures. In parallel, a neural network approach was tested to accomplish the same task. It was found that the hemispherical temperature correlates best to a liquid slag fraction of 85.0 wt% under reducing and 80.1 wt% under oxidizing conditions. The thermodynamic model is able to predict the hemispherical temperature under reducing conditions for 32% of the data while exclusion criteria defining the validity range have been formulated. The neural network model shows in average a higher accuracy of predicting ash fusion behavior from ash composition covering also temperatures of initial deformation and fluidity and appears as a suitable alternative to the thermodynamic calculation if sufficient data are available (i.e. covering the coal ash composition range of interest).

中文翻译：

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从用于气流气化的煤灰成分预测灰融合行为

摘要 在气流气化中，固体燃料与氧气和蒸汽接触，在 1250–1800 °C 的温度下产生结渣条件。工艺温度不能自由选择，而是由煤灰的熔化行为决定。通过混合不同的煤和助熔剂，可以降低灰熔融温度，从而允许在较低的反应器温度下运行并节省氧气。由于无法在线测量灰分融合行为，因此使用快速测量的煤灰成分并立即估计灰分融合行为是有益的。在这项工作中，调查了来自世界各地的 300 多种不同的煤样。这包括从 X 射线荧光 (XRF) 分析和还原和氧化条件下的标准灰融合行为确定的灰成分。在系统方法中，灰分成分仅限于最重要的成分以优化计算时间。ChemApp 软件用于基于 FToxid 和 FactPS 数据库计算热力学平衡。获得的结果涉及以 10%-pts 步骤计算的 10% 至 100% 的灰熔体为液态炉渣的温度。根据应用的气氛，获得的结果已与实验确定的融合温度在统计上相关。同时，测试了神经网络方法来完成相同的任务。发现半球温度与还原条件下 85.0 wt% 和氧化条件下 80.1 wt% 的液态炉渣分数最相关。热力学模型能够预测 32% 数据在还原条件下的半球温度，同时制定了定义有效范围的排除标准。神经网络模型平均显示出从灰成分预测灰融合行为的更高准确度，还涵盖初始变形和流动性的温度，如果有足够的数据（即覆盖煤灰成分范围兴趣）。