基于灰色预测模型的水煤浆输送管道冲蚀磨损寿命预测

doi:10.16085/j.issn.1000-6613.2022-2208

化工进展 ›› 2023, Vol. 42 ›› Issue (7): 3431-3442.DOI: 10.16085/j.issn.1000-6613.2022-2208

• 专栏：智能化工装备与安全 • 上一篇下一篇

基于灰色预测模型的水煤浆输送管道冲蚀磨损寿命预测

王硕¹^,²(), 张亚新²^,³(), 朱博韬¹^,²

^1.新疆大学化工学院，新疆乌鲁木齐 830000
^2.新疆大学煤炭清洁转化与化工过程自治区重点实验室，新疆乌鲁木齐 830000
^3.新疆大学省部共建碳基能源资源化学与利用国家重点实验室，新疆乌鲁木齐 830000

收稿日期:2022-11-29 修回日期:2023-03-17 出版日期:2023-07-15 发布日期:2023-08-14
通讯作者: 张亚新
作者简介:王硕（1996—），男，硕士研究生，研究方向为设备数值模拟与化工过程强化。E-mail：ws18522876700@163.com。
基金资助:
国家自然科学基金(21766034)

Prediction of erosion life of coal water slurry pipeline based on grey prediction model

WANG Shuo¹^,²(), ZHANG Yaxin²^,³(), ZHU Botao¹^,²

^1.College of Chemical Engineering, Xinjiang University, Urumqi 830000, Xinjiang, China
^2.Provincial Key Laboratory of Coal Clean Conversion and Chemical Process, Xinjiang University, Urumqi 830000, Xinjiang, China
^3.State Key Laboratory of Carbon-based Energy Resources Chemistry and Utilization, Xinjiang University, Urumqi 830000, Xinjiang, China

Received:2022-11-29 Revised:2023-03-17 Online:2023-07-15 Published:2023-08-14
Contact: ZHANG Yaxin

摘要/Abstract

摘要：

固液两相流广泛出现在煤化工生产过程中，管道输送系统受固液两相流介质冲蚀磨损尤为严重，系统突发性失效风险极高，已成为煤化工装置安全生产与稳定运行的重大隐患。降低生产安全风险，保障管道系统安全可靠运行，开展冲蚀磨损寿命预测研究是煤化工设备完整性研究的关键一环。本文以新疆某煤化工企业水煤浆汽化炉进料管为研究对象，应用ANSYS 2021R1软件进行数值模拟分析，确定弯管冲蚀磨损主要位置与短期内冲蚀磨损减薄量，为预测模型建立提供原始序列，通过灰色预测理论分别建立GM(1,1)模型、无偏GM(1,1)模型以及灰色马尔科夫模型完成对弯管冲蚀速率的预测，并结合SY/T 6151—2009《钢质管道管体腐蚀损伤评价方法》得出该管道服役寿命与检修周期。结果表明：冲蚀磨损减薄主要集中在弯管轴向70°左右，且在工艺条件相同的情况下冲蚀磨损减薄的最大位置不随时间变化发生移动。无偏GM(1,1)模型与灰色马尔科夫模型在中长期预测中优势显著，且灰色马尔科夫模型更适宜长期预测工作。所涉及的水煤浆汽化炉进料输送弯管服役寿命为570天左右，在弯管投入使用450天后冲蚀减薄量增长显著，需要定期开展该弯管重点部位的监测与预防性维护。

关键词: 两相流, 冲蚀磨损, 数值模拟, 灰色预测系统, 寿命预测

Abstract:

Solid-liquid two-phase flow is widely appeared in the coal chemical production process, two-phase solid-liquid flow erosion is a particular problem in the pipeline transportation system, and the potential for sudden system failure is extremely high and this has become a major hidden hazard for the safe production and stable operation of coal chemical plants. To reduce the production safety risk and ensure the safe and reliable operation of pipeline system, the study on the prediction of erosion life was a key part of the research on the integrity of coal chemical equipment. Taking a coal-water slurry vaporizer feed pipe of a coal chemical plant in Xinjiang as the research object, ANSYS 2012R1 software is applied to conduct numerical simulation analysis to determine the main location of the bend tube erosion and the erosion thinning in a short period of time, to provide the original sequence for the prediction model, and to establish GM(1,1) model, unbiased GM(1,1) model and gray Markov model respectively through gray prediction theory. The prediction of the erosion rate of the pipe bend was completed, and the service life and maintenance cycle of the pipe were derived by combining the “Assessment of corroded steel pipelines”. This showed that the erosion thinning was mainly concentrated in the axial direction of the bend about 70 degrees, and the maximum position of the erosion thinning did not move with the change of time under the same process conditions. The unbiased GM(1,1) model and the Gray Markov model had significant advantages in medium and long-term prediction, and the Gray Markov model was more suitable for long-term prediction work. The service life of the coal-water slurry vaporizer feed transfer bend involved was about 570 days, and the amount of erosion thinning increased significantly after the bend was put into service for 450 days, which required regular monitoring and preventive maintenance of the key parts of the bend.

Key words: two-phase flow, erosion, numerical simulation, grey prediction theory, life prediction

中图分类号:

TQ051

王硕, 张亚新, 朱博韬. 基于灰色预测模型的水煤浆输送管道冲蚀磨损寿命预测[J]. 化工进展, 2023, 42(7): 3431-3442.

WANG Shuo, ZHANG Yaxin, ZHU Botao. Prediction of erosion life of coal water slurry pipeline based on grey prediction model[J]. Chemical Industry and Engineering Progress, 2023, 42(7): 3431-3442.

图/表 17

参考文献 35

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预测精度评价	P	C
好	≥0.95	<0.35
合格	>0.80	<0.50
勉强	>0.70	<0.65
不合格	≤0.70	≥0.65

预测精度评价	P	C
好	≥0.95	<0.35
合格	>0.80	<0.50
勉强	>0.70	<0.65
不合格	≤0.70	≥0.65

序号	服役时间/d	冲蚀磨损减薄量/mm
1	30	0.2120
2	60	0.5890
3	90	0.7850
4	120	1.3586
5	150	1.5027
6	180	1.6381
7	210	2.1890
8	240	2.3577
9	270	2.6980
10	300	2.8972
11	330	3.0450
12	360	3.4768

序号	服役时间/d	冲蚀磨损减薄量/mm
1	30	0.2120
2	60	0.5890
3	90	0.7850
4	120	1.3586
5	150	1.5027
6	180	1.6381
7	210	2.1890
8	240	2.3577
9	270	2.6980
10	300	2.8972
11	330	3.0450
12	360	3.4768

时间/d	GM(1,1)				无偏GM(1,1)				灰色马尔科夫模型
时间/d	预测值 /mm	残差	相对误差 /%	平均误差 /%	预测值 /mm	残差	相对误差 /%	平均误差 /%	预测值 /mm	残差	相对误差 /%	平均误差 /%
30	0.2120	0.0000	0.00	4.89	0.2120	0.0000	0.00	4.05	0.2120	0.0000	0.00	0.77
60	0.6325	-0.0435	-7.39		0.7291	-0.1401	-13.79		0.5921	-0.0031	-0.49
90	0.8042	-0.0192	-2.45		0.7962	-0.0112	-1.423		0.7928	-0.0078	-0.97
120	1.3058	0.0528	3.89		1.3125	0.0461	3.39		1.3675	-0.0089	-0.69
150	1.4895	0.0132	0.88		1.4975	0.0052	0.35		1.5110	-0.0083	-0.55
180	1.6990	-0.0609	-3.72		1.7340	-0.0959	-5.85		1.6752	-0.0371	-2.18
210	1.9381	0.2509	11.46		2.0175	0.1715	7.83		2.1235	0.0655	3.38
240	2.2100	0.1477	6.26		2.2500	0.1077	4.57		2.4214	-0.0637	-2.88
270	2.5210	0.1770	6.56		2.5450	0.1530	5.67		2.7621	-0.0641	-2.54
300	2.8765	0.0207	0.71		2.8837	0.0135	0.47		2.9179	-0.0207	-0.72
330	3.2812	-0.2362	-7.76		3.1462	-0.1012	-3.32		3.0719	-0.0269	-0.82
360	3.7428	-0.2660	-7.65		3.5430	-0.0662	-1.90		3.5040	-0.0272	-0.73

基于灰色预测模型的水煤浆输送管道冲蚀磨损寿命预测

Prediction of erosion life of coal water slurry pipeline based on grey prediction model

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图/表 17

参考文献 35

相关文章 15

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Metrics

本文评价

状态	区间分级	误差区间
E₁	[MIN, u-s)	[-7.76, -5.87)
E₂	[u-s, u+0.5s)	[-5.87, 3.03)
E₃	[u+0.5s, u+s)	[3.03, 6.00)
E₄	[u+s, MAX)	[6.00, 11.46)

服役状态	最大减薄量
轻度冲蚀	<1mm
中度冲蚀	1～2mm
重度冲蚀	2mm～50%公称壁厚
严重冲蚀	50%～80%公称壁厚
冲蚀穿孔	>80%公称壁厚

编号	最大冲蚀位置		最大减薄量 /mm	预测值相对误差/%
编号	轴向角度 /(°)	偏离对称面距离^① /mm	最大减薄量 /mm	预测值相对误差/%
1^#	70	0	5.91	6.42
2^#	70	0	5.68	1.43
3^#	70	-2.1	5.66	1.07
4^#	72	-3	5.75	2.68
5^#	70	0	5.70	1.78
6^#	70	-2	5.84	4.28

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序号	状态概率				最大概率结果
序号	E₁	E₂	E₃	E₄	最大概率结果
13	0.6667	0.3334	0	0	E₁
14	0.5778	0.2667	0.0667	0.0667	E₁
15	0.4900	0.3339	0.0080	0.0969	E₁

序号	状态概率				最大概率结果
序号	E₁	E₂	E₃	E₄	最大概率结果
13	0.6667	0.3334	0	0	E₁
14	0.5778	0.2667	0.0667	0.0667	E₁
15	0.4900	0.3339	0.0080	0.0969	E₁