化工进展 ›› 2020, Vol. 39 ›› Issue (9): 3617-3625.DOI: 10.16085/j.issn.1000-6613.2019-1920

• 能源加工与技术 • 上一篇    下一篇

酸性氧化物和酸碱比对煤灰熔融行为的影响

郑烨1,2(), 李建波3, 张锴1,2(), 关彦军1, 杨凤玲4, 程芳琴4   

  1. 1.华北电力大学热电生产过程污染物监测与控制北京市重点实验室,北京 102206
    2.华北电力大学电站能量传递转化与系统教育部重点实验室,北京 102206
    3.重庆大学低品位能源利用技术及系统教育部重点实验室,重庆 400044
    4.山西大学资源与环境工程研究所,国家环境保护煤炭废弃物资源化高效利用技术重点实验室,山西 太原 030006
  • 出版日期:2020-09-05 发布日期:2020-09-11
  • 通讯作者: 张锴
  • 作者简介:郑烨(1987—),男,博士研究生,研究方向为煤灰化学。E-mail:1142102039@ncepu.edu.cn
  • 基金资助:
    国家自然科学基金委与山西煤基低碳联合基金重点项目(U1910215);中央高校基本科研业务费专项(2018ZD03)

Effect of acid oxides and acid to basic ratio on ash fusion characteristics

Ye ZHENG1,2(), Jianbo LI3, Kai ZHANG1,2(), Yanjun GUAN1, Fengling YANG4, Fangqin CHENG4   

  1. 1.Beijing Key Lab of Emission Surveillance and Control for Thermal Power generation, North China Electric Power University, Beijing 102206, China
    2.Key Lab of Power Station Energy Transfer Conversion and System (North China Electric Power University), MOE, Beijing 102206, China
    3.Key Lab of Low-grade Energy Utilization Technologies and Systems of MOE, Chongqing University, Chongqing 400044, China
    4.State Environmental Protection Key Lab of Efficient Utilization Technology of Coal Waste Resources, Institute of Resource and Environment Engineering, Shanxi University, Taiyuan 030006, Shanxi, China
  • Online:2020-09-05 Published:2020-09-11
  • Contact: Kai ZHANG

摘要:

为了研究酸性氧化物(SiO2和Al2O3)的相对含量和酸碱比对煤灰熔融行为的影响,本文以准东煤灰化学组成为基础,利用分析纯试剂制备了28组合成灰样品,在马弗炉815℃灰化后利用灰熔融特性分析仪、扫描电子显微镜-能谱仪(SEM-EDS)和X射线衍射仪(XRD)对样品熔融特性、表观形貌和矿物组成进行表征。进而采用多元线性回归法建立了灰熔融温度预测模型,并对该模型的适用性进行了检验。结果表明:在相同酸碱比下,当SiO2含量由9%上升至33.73%,而Al2O3含量由35.98%降至11.25%时,合成灰的变形温度(DT)、软化温度(ST)、半球温度(HT)和流动温度(FT)均呈单调下降趋势,这意味着SiO2含量的增加可能促进了煤灰的熔融过程;在不同碱酸比下,合成灰的熔融温度随着酸碱比的增加呈先下降后升高的变化趋势,在酸碱比为1.25时合成灰的特征温度出现最小值,表明酸碱比对合成灰熔融温度的影响呈非线性关系。通过SEM-EDS和XRD表征发现,合成灰中CaO、Fe2O3、Ca2MgSiO7、Ca2Fe2O5、SiO2和Al2O3等耐熔矿物和CaSiO3等助熔矿物的相对含量以及与钠相关的低温共熔反应是改变合成灰熔融温度的主要因素。本文所建立模型对文献中6组灰样4个特征温度的预测结果与其对应测量值之间最大残差绝对值均小于80℃,说明该模型在本文的合成灰化学组分范围内可用性较好,具有一定的应用价值。

关键词: 合成灰, 灰熔融温度, 矿物组成, 微观形貌, 预测模型

Abstract:

The influence of acid oxide content and acid to basic ratio (A/B) on ash fusion characteristics were investigated by using twenty-eight synthetic ashes comprising of the main chemical compositions of a typical Zhundong coal. The synthetic samples were firstly ashed in a muffle furnace at 815℃, and then subjected to ash fusion analyzer, SEM-EDS and XRD analysis for their fusion temperatures (AFTs), morphological and mineralogical characteristics. Furthermore, a set of prediction models for four fusion temperatures was established by using regression analysis method, based on their chemical compositions and corresponding fusion temperatures of the ash samples. The results showed that at the same A/B ratio, the deformation temperature (DT), softening temperature (ST), hemispherical temperature (HT) and flow temperature (FT) of synthetic ashes decreased remarkably when SiO2 content increased from 9% to 33.73%, while Al2O3 decreased from 35.98% to 11.25%, indicating that the addition of SiO2 might promote ash fusions. While at different A/B ratios, the AFTs of synthetic ashes decreased first, reaching the minimum AFTs at A/Bof 1.25, but increased afterwards with increasing A/B ratio, exhibiting a non-linear correlation between A/B and ATFs. The results from SEM-EDS and XRD revealed that the fusion characteristics of the synthetic ashes were mainly dependent on the refractory minerals such as CaO, Fe2O3, Ca2MgSiO7, Ca2Fe2O5, SiO2 and Al2O3,and the fluxing minerals such as CaSiO3, as well as the degree of eutectic formation associated with Na-bearing minerals. The absolute residual errors between the predicted values by the models suggested in this study and the measured results of six ash samples in literature were within 80℃ (abs), suggesting that the current prediction models are applicable for predicting coal ash fusion temperatures, as long as its chemical composition is within the current experimental range.

Key words: synthetic ash, ash fusion temperatures, mineral composition, micromorphology, prediction models

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