| 基于全子集回归和逐步回归的煤灰氧化物组成对熔融性影响的研究 |
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| 引用本文： | 李吉辉,刘若琛,马嘉成,黄根,马力强.基于全子集回归和逐步回归的煤灰氧化物组成对熔融性影响的研究[J].煤炭工程,2020,52(6):153-158. |
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| 作者姓名： | 李吉辉 刘若琛 马嘉成 黄根 马力强 |
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| 作者单位： | 1. 中国矿业大学（北京）化学与环境工程学院;2. 中国矿业大学（北京）; |
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| 摘 要： | 为探究氧化物组成对煤灰熔融特性的影响，选取煤灰中的氧化物含量作为自变量，在SPSS软件平台上对变形温度DT、软化温度ST、半球温度HT和流动温度FT分别进行全子集回归和逐步回归，比较得到显著性最强的新定义的熔融指数FI和最优的回归预测方程。结果表明，单一氧化物组分对灰熔温度的影响不显著|对DT影响最显著的熔融指数为FID=Al2O3+Fe2O3，且煤灰中FID含量低于30%时，DT几乎不变化，含量大于30%时DT发生较大幅度降低|对FT影响最显著的熔融指数为FIF=SiO2+Al2O3+Fe2O3，且随着FIF含量升高，流动温度呈上升趋势|对半球温度HT影响最显著的熔融指数FIH= SiO2+Al2O3，对软化温度ST影响最显著的熔融指数FIS=SiO2+Al2O3+Fe2O3，但FIH和FIS对ST和HT的显著性略低，为得到更准确的预测模型，进一步以十种氧化物为起点通过逐步回归方法分析得到ST和HT的预测方程。
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| 关 键 词： | 煤灰熔融性 回归分析 SPSS 灰熔温度 熔融指数 |
| 收稿时间： | 2020-05-25 |
| 修稿时间： | 2020-06-12 |
Full Subset Regression Study of Oxide Components and Content in Coal on Ash Fusibility Based on SPSS |
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| Abstract: | To explore the effect of oxide composition on coal ash fusion characteristics, the oxide content in coal ash was selected as independent variables. On the platform of SPSS software, full subset regression and stepwise regression were carried out on deformation temperature DT, softening temperature ST, hemispheric temperature HT and flow temperature FT, respectively. The newly defined fusion index FI and the optimal regression prediction equation were obtained. The results show that the single oxide component has no significant effect on the ash melting temperature. The melt index with the most significant effect on DT is FID=Al2O3+Fe2O3, and when the FID content in coal ash is less than 30%, DT hardly changes, and when the content is greater than 30%, DT decreases greatly. The melt index with the most significant effect on FT is FIF=SiO2+Al2O3+Fe2O3, and as the FIF content increases, the flow temperature shows an upward trend. The melt index FIH=SiO2+Al2O3 which has the most significant influence on the hemispheric temperature HT, and the melt index FIS=SiO2+Al2O3+Fe2O3 which has the most significant influence on the softening temperature ST, but the significance of FIH and FIS on ST and HT is slightly lower. For a more accurate prediction model, the prediction equations of ST and HT are obtained by stepwise regression method analysis starting with ten oxides. |
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