配煤对高熔点煤灰熔融特性影响的研究

谢良才; 李风海; 薛兆民; 徐龙; 马晓迅

配煤对高熔点煤灰熔融特性影响的研究

1.
西北大学化工学院陕北能源先进化工利用技术教育部工程研究中心, 陕西省洁净煤转化工程技术中心, 西安市能源高效清洁化工利用工程实验室, 陕西西安 710069
2.
菏泽学院化学化工系, 山东菏泽 274015

基金项目:

国家自然科学基金重点项目 21536009

山东省自然科学基金 ZR2014BM014

西安市科技计划项目 CXY1511（4）

详细信息

中图分类号: TQ534
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- 被引次数: 0
出版历程
- 收稿日期: 2016-07-18
- 修回日期: 2016-09-17
- 网络出版日期: 2021-01-23
- 刊出日期: 2016-12-10

Influence of coal blending on ash fusion characteristics for coal with high ash fusion temperature

1.
School of Chemical Engineering, Northwest University, Chemical Engineering Research Center of the Ministry of Education for Advanced Use Technology of Shanbei Energy, Shaanxi Research Center of Engineering Technology for Clean Coal Conversion, Xi'an Engineering Laboratory for Energy Efficient and Clean Chemical Utilization, Xi'an 710069, China
2.
Department of Chemistry and Chemistry Engineering, Heze University, He'ze 274015, China

More Information

Corresponding author: Tel: 029-88307657, longxuxulong@163.com

摘要

摘要: 采用灰熔点较低的襄阳煤和灰熔点较高的晋城无烟煤组成的混合煤样，利用XRF、SEM、DSC、XRD、三元相图等分析方法，探究了襄阳煤对晋城无烟煤煤灰熔融温度的影响。结果表明，配煤能有效降低高熔点煤灰的熔融温度，当襄阳煤的加入量小于24%时，混合煤灰熔融温度显著降低；襄阳煤的加入量在24%-40%时，混合煤灰熔融温度变化平缓且流动温度低于1 400℃。混合煤灰中的成分在1 000-1 200℃发生一系列的化学反应，主要包括SiO₂与Al₂O₃结合产生高熔点物质莫来石以及Fe₂O₃、CaO与莫来石反应转化形成铁尖晶石、钙长石等新物质，由此造成了煤灰熔融温度的变化。基于BP神经网络对实验数据建立预测模型，其预测效果优于前人总结的经验公式，平均准确度高于99%。利用热力学软件HSC 5.0分析了CaO、Fe₂O₃对降低煤灰熔融温度的影响，分析表明，CaO对莫来石的转化作用优于Fe₂O₃。
- 配煤 /
- 灰熔融温度 /
- BP神经网络
Abstract: Xiangyang coal with low ash fusion temperature (AFT) and Jincheng coal with high AFT were used to prepare the blending samples. The influence of Xiangyang coal addition on AFT of Jincheng coal was explored by XRF, SEM, DSC, XRD, and ternary phase diagram analysis. The results show that blending coal can reduce the AFT effectively. The AFT of blending coal is lowered significantly when the adding amount of Xiangyang coal is lower than 24%. Whereas, when the adding amount is between 24% and 40%, AFT of the mixed coal has a slight change and the ash flow temperature is below 1 400℃. A series of chemical reactions among ash composition of mixed coal occur at 1 000-1 200℃, mainly including formation of high melting point compound (mullite) from SiO₂ with A1₂O₃, and that of low melting point compounds (anorthite and hercynite) from the reactions between mullite and CaO or Fe₂O₃. The above reactions mainly cause the changes of ash fusion temperature in blending coal. Based on BP neural network, a prediction model of ash fusion temperature was built. It is proved that the prediction average accuracy by BP neural network is higher than 99%, which is better than that of a previous empirical formula. Furthermore, analysis by thermodynamics software (HSC 5.0) shows that mullite prefers to react with CaO rather than Fe₂O₃.
- coal blending /
- ash fusion temperature /
- BP neural network

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图 1 加入不同质量的襄阳煤对晋城无烟煤煤灰熔融温度的影响

Figure 1 Ash fusion temperature of Jincheng coal with addition of Xiangyang coal

(a): experimental data; (b): predicted data by BP neural network model

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图 2 SiO₂-Al₂O₃-CaO三元相图

Figure 2 Ternary phase diagram of SiO₂-Al₂O₃-CaO

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图 3 煤灰FT预测值与实验值对比

Figure 3 Comparison of FT between experimental results and predictive data based on different models

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图 4 晋城无烟煤与襄阳煤混合灰渣放大5000倍的微观形貌

Figure 4 Magnified 5000 times microcosmic morphology of ash residue for blending coal

(a): 30% Xiangyang coal; 1100℃; (b): 20% Xiangyang coal; 1100℃; (c): 30% Xiangyang coal; 1200℃; (d): 20% Xiangyang coal; 1200℃

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图 5 晋城无烟煤与襄阳煤混合煤灰的DSC曲线

Figure 5 DSC curves of blending coal ash

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图 6 不同含量的襄阳煤在1100℃(a)、1200℃(b)、1350℃(c) 的XRD谱图

Figure 6 XRD pattern of different quantity of Xiangyang coal ash at 1100℃(a), 1200℃ (b) and 1350℃(c)

1: quartz, SiO₂; 2: rankinite, Ca₃Si₂O₇; 3: mullite, Al₆Si₂O₁₃; 4: anorthite, CaAl₂Si₂O₈; 5: sillimanite, Al₂SiO₅; 6: anhydrite, CaSO₄; 7: mayenite, Ca₁₂Al₁₄O₃₃; 8: calcium iron oxide, CaO·Fe₃O₄; 9: alumina, Al₂O₃; 10: fayalite, Fe₂SiO₄; 11: calcium iron oxide, CaO·Fe₂O₃; 12: hercynite, FeAl₂O₄; 13: yeelimite, Ca₄Al₆O₁₂SO₄; 14: clinotobermorite, Ca₅Si₆O₁₇ JC: Jincheng coal; XY: Xiangyang coal

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图 7 不同温度下30%襄阳煤 (a) 和20%襄阳煤 (b) 的XRD谱图

Figure 7 XRD patterns of 30% Xiangyang coal ash (a) and 20% Xiangyang coal ash (b) at different temperatures

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图 8 BP神经网络对于CaO、Fe₂O₃、FT三者关系的预测图

Figure 8 Predictive effects of CaO, Fe₂O₃ on FT based on BP

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图 9 主要化学反应的热力学分析

Figure 9 Thermodynamic analysis of the main chemical reaction

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表 1 晋城无烟煤和襄阳煤的工业分析与元素分析

Table 1 Proximate and ultimate analysis of coal samples

Coal sample	Proximate analysis w_ad/%				Ultimate analysis w/%
Coal sample	M	A	V	FC	C	H	N	S	O^*
Jincheng	1.17	17.40	9.08	72.35	73.31	2.87	1.07	0.79	3.39
Xiangyang	10.06	13.36	34.04	42.54	49.63	3.76	0.70	0.28	22.21
^*: by difference

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表 2 晋城无烟煤和襄阳煤的煤灰成分分析

Table 2 Ash composition of coal samples

Coal sample	Ash composition w/%
Coal sample	SiO₂	A1₂O₃	Fe₂O₃	CaO	MgO	SO₃	K₂O	Na₂O	TiO₂	P₂O₃
Jincheng	47.00	33.55	7.99	5.16	1.60	2.92	0.38	0.46	0.85	0.01
Xiangyang	31.06	14.32	18.95	28.62	2.98	0.99	2.08	0.20	0.74	0.16

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表 3 不同质量的襄阳煤与晋城无烟煤组成的混合煤灰的酸碱比

Table 3 Ratio of acid to alkali in blending ash with addition of Xiangyang coal into Jincheng coal

Xiangyang coal w/%	0	10	20	24	26	28	30	32	34	36	40	50	100
Ratio of acid to alkali (A/B)	5.22	4.32	3.51	3.25	3.15	3.04	2.94	2.82	2.74	2.63	2.47	2.08	0.87
Ratio of acid to alkali (x)	5.52	4.42	3.69	3.41	3.31	3.20	3.08	2.97	2.88	2.76	2.59	2.18	0.91
note： $\begin{align} & \text{A/B=}\frac{\text{Si}{{\text{O}}_{2}}\text{+A}{{\text{l}}_{2}}{{\text{O}}_{3}}\text{+Ti}{{\text{O}}_{2}}}{\text{F}{{\text{e}}_{2}}{{\text{O}}_{3}}\text{+CaO+}{{\text{K}}_{2}}\text{O+MgO+N}{{\text{a}}_{2}}\text{O}};x=\frac{\text{Si}{{\text{O}}_{2}}\text{+A}{{\text{l}}_{2}}{{\text{O}}_{3}}\text{+Ti}{{\text{O}}_{2}}}{\text{F}{{\text{e}}_{2}}{{\text{O}}_{3}}\text{+CaO}+\text{MgO}} \\ & \\ \end{align}$

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表 4 经验公式和BP神经网络的预测准确度

Table 4 Forecast precision of empirical formulas and BP neural network

Prediction models	(1)	(2)	(3)	BP
K /%	92.68	97.40	95.06	99.12
K_min/%	91.15	96.26	93.46	98.17

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