油页岩添加对准东高钠煤灰熔融行为影响的研究

doi:10.11949/0438-1157.20230114

化工学报 ›› 2023, Vol. 74 ›› Issue (5): 2123-2135.DOI: 10.11949/0438-1157.20230114

油页岩添加对准东高钠煤灰熔融行为影响的研究

黄磊¹^,²(), 孔令学¹^,³(), 白进¹, 李怀柱¹, 郭振兴¹, 白宗庆¹, 李平³, 李文¹

^1.中国科学院山西煤炭化学研究所，煤转化国家重点实验室，山西太原 030001
^2.中国科学院大学，北京 100049
^3.宁夏大学，省部共建煤炭高效利用与绿色化工国家重点实验室，宁夏银川 750021

收稿日期:2023-02-15 修回日期:2023-04-14 出版日期:2023-05-05 发布日期:2023-06-29
通讯作者: 孔令学
作者简介:黄磊（1995—），男，博士研究生，18361220739@163.com
基金资助:
国家自然科学基金项目(22178370);山西煤化所创新基金项目(SCJC-DT-2022-01);省部共建煤炭高效利用与绿色化工国家重点实验室开放课题项目(2021-K21)

Effect of oil shale addition on ash fusion behavior of Zhundong high-sodium coal

Lei HUANG¹^,²(), Lingxue KONG¹^,³(), Jin BAI¹, Huaizhu LI¹, Zhenxing GUO¹, Zongqing BAI¹, Ping LI³, Wen LI¹

^1.State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, Shanxi, China
^2.University of Chinese Academy of Science, Beijing 100049, China
^3.State Key Laboratory of High-efficiency Coal Utilization and Green Chemical Engineering, Ningxia University, Yinchuan 750021, Ningxia, China

Received:2023-02-15 Revised:2023-04-14 Online:2023-05-05 Published:2023-06-29
Contact: Lingxue KONG

摘要/Abstract

摘要：

准东煤中碱金属钠的含量高，在气化过程中生成的飞灰导致换热器积灰、腐蚀等问题的发生，对气化炉的安全稳定运行带来极大的挑战。煤灰的熔融行为是阐明飞灰沉积行为的重要参数，选取含Si、Al较高的油页岩为添加剂，研究了油页岩添加对准东高钠煤灰熔融行为的影响及机理，探讨使用油页岩解决准东高钠煤气化利用中的飞灰沉积问题。结果表明，随着油页岩添加量的增加，准东高钠煤红沙泉和将二矿煤灰的初始烧结温度与液相烧结温度区间长度增加，煤灰的沉积倾向减轻；矿物质分析显示，油页岩的添加降低了初始煤灰中硫酸钙的含量，提高石英的含量，同时熔融过程中生成大量夕线石，是准东高钠煤灰熔点升高的主要原因。对于红沙泉与将二矿煤，分别添加15%和20%的油页岩可有效提高煤灰的熔融特征温度，避免在气化过程中飞灰发生沉积。最后，基于神经网络模型，建立了准东高钠煤添加油页岩后的煤灰变形温度(DT)及流动温度(FT)的预测模型。

关键词: 准东煤, 油页岩, 沉积物, 煤灰熔融行为, 矿物质, 神经网络, 结垢

Abstract:

The content of alkali metal sodium in Zhundong coal is high, and the fly ash generated during the gasification process leads to problems such as dust accumulation and corrosion in the heat exchanger, which poses a great challenge to the safe and stable operation of the gasifier. Ash fusion behavior is an important parameter to reveal the ash deposition behavior. In this study, oil shale (OS) as an additive with high contents of Si and Al was selected to investigate its effect on the ash fusion behavior of Zhundong high-sodium coal. The results showed that the initial sintering temperature and liquid phase sintering temperature section of Hongshaquan (HSQ) coal and Jiangerkuang (JEK) coal were increased with the increasing OS additions, which alleviate the deposition tendency of coal ash. Minerals analysis illustrated that the addition of OS decreased the anhydrite content and increased the quartz content in raw coal ashes. Meanwhile, sillimanite was formed, which increases the ash fusion temperatures (AFTs) of Zhundong high-sodium coal. For HSQ and JEK coal, when the OS addition was 15% and 20%, the ash deposition can be effectively avoided for the elevated AFTs. Finally, the prediction of deformation temperature and flow temperature was conducted based on artificial neural network and ash compositions.

Key words: Zhundong coal, oil shale, deposition, ash fusion behavior, mineral, neural network, fouling

中图分类号:

TQ 54

黄磊, 孔令学, 白进, 李怀柱, 郭振兴, 白宗庆, 李平, 李文. 油页岩添加对准东高钠煤灰熔融行为影响的研究[J]. 化工学报, 2023, 74(5): 2123-2135.

Lei HUANG, Lingxue KONG, Jin BAI, Huaizhu LI, Zhenxing GUO, Zongqing BAI, Ping LI, Wen LI. Effect of oil shale addition on ash fusion behavior of Zhundong high-sodium coal[J]. CIESC Journal, 2023, 74(5): 2123-2135.

图/表 14

表1 红沙泉、将军庙二矿煤与油页岩的工业分析及元素分析

Table 1 Proximate and ultimate analyses of HSQ， JEK and OS

Coal	Proximate analysis/ %(mass, air dried)				Ultimate analysis/ % (mass, dry ash free)
Coal	M	A	V	FC	S_t	C	H	N	O*
HSQ	2.10	13.04	27.72	57.14	1.89	79.00	4.14	0.90	14.07
JEK	1.12	6.70	29.25	62.93	0.56	73.33	4.05	0.82	21.24
OS	1.78	71.92	18.55	7.75	0.68	57.11	6.92	0.49	34.79

表2 红沙泉、将军庙二矿煤和油页岩的灰化学组成

Table 2 Ash chemical compositions of HSQ， JEK and OS

Coal	含量/%(mass)
Coal	SiO₂	Al₂O₃	Fe₂O₃	CaO	MgO	TiO₂	SO₃	K₂O	Na₂O	P₂O₅
HSQ	45.59	13.89	13.15	8.18	3.62	0.68	8.48	1.02	3.35	0.56
JEK	18.25	9.81	24.93	11.96	6.50	0.52	19.45	0.35	6.36	0.06
OS	63.32	28.12	2.25	2.05	0.31	1.22	0.40	0.22	0.40	0.09

表3 红沙泉和将军庙二矿煤中Na与Ca的赋存形态

Table 3 Occurrence forms of Na and Ca in HSQ and JEK coals

Element		含量/(μg/g)
Element		Water-soluble	NH₄Ac-soluble	HCl-soluble	Insoluble
HSQ	Na	2645.00	1250.00	810.00	725.50
HSQ	Ca	105.00	5027.50	1485.00	240.00
JEK	Na	3797.50	2425.00	2122.50	880.00
JEK	Ca	52.50	6130.00	2857.50	310.00

图1 神经网络原理

Fig.1 Principle of artificial neural network

图2 HSQ和JEK煤灰的TMA曲线

Fig.2 Shrinkage curves of HSQ and JEK coal ashes

图3 不同温度下准东高钠煤灰的矿物质组成1—anhydrite (CaSO4); 2—hematite (Fe2O3); 3—magnetite (Fe3O4); 4—nepheline (NaAlSiO4); 5—quartz (SiO2); 6—feldspar (NaAlSi3O8, CaAl2Si2O8); 7—diopside (CaMgSi2O6); 8—sillimanite (Al2SiO5); 9—Na8Al6(SiO4)6SO4; 10—gehlenite (Ca2Al2SiO7); 11—rhodolite (Mg3Al2(SiO4)3)

Fig.3 Minerals of HSQ and JEK coal ashes under different temperatures

图4 添加不同比例油页岩时HSQ和JEK煤灰的AFTs

Fig.4 AFTs of HSQ and JEK coal ashes with different OS additions

图5 添加不同比例油页岩后HSQ和JEK煤灰的熔融行为及特征温度

Fig.5 Shrinkage curves and characteristic temperatures of HSQ and JEK coal ashes with different OS addition

图6 添加不同比例油页岩时HSQ煤灰的XRD谱图1—anhydrite (CaSO4); 2—hematite (Fe2O3); 3—magnetite (Fe3O4); 4—nepheline (NaAlSiO4); 5—quartz (SiO2); 6—feldspar (NaAlSi3O8, CaAl2Si2O8); 7—diopside (CaMgSi2O6); 8—sillimanite (Al2SiO5)

Fig.6 XRD patterns of HSQ coal ash with different OS additions

图7 添加不同比例油页岩JEK煤灰的XRD谱图1—anhydrite (CaSO4); 2—hematite (Fe2O3); 3—magnetite (Fe3O4); 4—nepheline (NaAlSiO4); 5—quartz (SiO2); 6—feldspar (NaAlSi3O8, CaAl2Si2O8); 7—diopside (CaMgSi2O6); 8—sillimanite (Al2SiO5); 9—Na8Al6(SiO4)6SO4; 10—gehlenite (Ca2Al2SiO7); 11—rhodolite [Mg3Al2(SiO4)3]

Fig.7 XRD patterns of JEK coal ash with different OS additions

图8 添加不同比例油页岩的HSQ煤灰FactSage计算结果

Fig.8 FactSage results of HSQ coal ash with different OS additions

图9 添加不同比例油页岩的JEK煤灰FactSage计算结果

Fig.9 FactSage results of JEK coal ash with different OS additions

表4 样本煤灰的化学组成

Table 4 Chemical compositions of coal ash samples

No.	S+A	Fe₂O₃	CaO	MgO	Na₂O	K₂O	SO₃	S/A	DT/℃	FT/℃
1	23.63	22.30	33.59	2.37	0.79	0.05	12.49	1.76	1272.00	1290.00
2	66.31	10.32	11.27	2.94	1.40	0.85	2.97	2.39	1243.00	1305.00
3	44.37	19.14	12.72	5.02	4.44	0.63	11.65	2.33	1162.00	1275.00
4	61.42	9.01	13.36	5.04	1.34	1.26	6.47	2.59	1204.00	1322.00
5	76.90	5.35	2.70	0.66	0.86	1.49	0.78	1.90	1500.00	1500.00
6	72.41	4.46	18.92	0.55	0.72	1.24	0.65	1.90	1335.00	1383.00
7	72.41	8.63	14.75	0.55	0.72	1.24	0.65	1.90	1332.00	1369.00
8	72.41	12.79	10.58	0.55	0.72	1.24	0.65	1.90	1298.00	1331.00
9	72.41	16.96	6.42	0.55	0.72	1.24	0.65	1.90	1317.00	1359.00
10	72.41	21.23	2.25	0.55	0.72	1.24	0.65	1.90	1381.00	1425.00
11	75.16	10.28	11.03	0.27	0.17	0.35	0.36	1.20	1408.00	1430.00
12	75.16	18.62	2.69	0.27	0.17	0.35	0.36	1.20	1510.00	1540.00
13	75.16	1.95	19.36	0.27	0.17	0.35	0.36	1.20	1474.00	1493.00
14	90.19	2.34	3.23	0.23	0.20	0.42	0.43	1.20	1510.00	1540.00
15	75.16	18.62	2.69	0.27	0.17	0.35	0.36	1.20	1483.00	1515.00
16	75.16	14.45	6.86	0.27	0.17	0.35	0.36	1.20	1463.00	1487.00
17	75.16	10.28	11.03	0.27	0.17	0.35	0.36	1.20	1408.00	1430.00
18	75.16	6.12	15.19	0.27	0.17	0.35	0.36	1.20	1405.00	1427.00
19	75.16	1.95	19.36	0.27	0.17	0.35	0.36	1.20	1438.00	1456.00
20	75.16	1.95	19.36	0.27	0.17	0.35	0.36	1.20	1474.00	1493.00
21	23.63	22.30	33.59	2.37	0.79	0.05	12.49	1.76	1272.00	1290.00

图10 神经网络训练结果及预测值

Fig.10 Training results and predictions of DT and FT by artificial neural network

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油页岩添加对准东高钠煤灰熔融行为影响的研究

Effect of oil shale addition on ash fusion behavior of Zhundong high-sodium coal

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