基于支持向量机的导管泄爆容器压力峰值预测

张庆武; 蒋军成; 喻源; 崔益虎

doi:10.11883/1001-1455(2014)06-0748-06

基于支持向量机的导管泄爆容器压力峰值预测

doi: 10.11883/1001-1455(2014)06-0748-06

南京工业大学城市建设与安全工程学院，江苏南京210009

基金项目: 国家自然科学基金项目（20976081，50904037）；江苏省高校自然科学基金项目（10KJB620001，12KJB620001）；江苏省2012年度普通高校研究生科研创新计划项目（CXZZ12－0452）

详细信息

作者简介:
张庆武(1985—), 男, 博士研究生

中图分类号: O389; TQ086; X932
计量
- 文章访问数: 3136
- HTML全文浏览量: 362
- PDF下载量: 266
- 被引次数: 0
出版历程
- 收稿日期: 2013-04-12
- 修回日期: 2013-06-08
- 刊出日期: 2014-11-25

Prediction of peak pressure in the explosion_-vented vessel with a venting duct based on support vector machine

School of Urban Construction and Safety Engineering, Nanjing University of Technology, Nanjing210009, Jiangsu, China

Funds: Supported bythe National Natural Science Foundation of China (20976081, 50904037)

More Information

Corresponding author: Jiang Jun-cheng, jcjiang@njut.edu.cn

摘要

摘要: 为了预测导管泄爆容器压力峰值，根据文献提取出影响导管泄爆容器压力峰值的因素，将这些因素作为输入变量，采用支持向量机算法对压力峰值与各因素的内在关系进行了研究，建立导管泄爆容器压力峰值预测模型，对模型的有效性及预测能力进行了验证。将预测模型与现有经验公式进行比较，表明支持向量机模型具有较好的预测能力，且预测能力优于经验公式。
- 爆炸力学 /
- 压力峰值 /
- 支持向量机 /
- 泄爆容器 /
- 导管 /
- 气体爆炸
Abstract: To predict the peak pressure in the explosion－vented vessel with a venting duct, the influencing factors on the peak pressure were abstracted from the experimental data in literatures.The abstracted factors were deployed as the inputs to the support vector machine(SVM), and the corresponding peak pressures were used as the outputs.Thereby, the SVM model was developed.The validity of the SVM model was checked by comparing the predictive capacities between the SVM model and the empirical formula.The results show that the SVM model has a better predictive capacity than the empirical formula.
- mechanics of explosion /
- peak pressure /
- support vector machine /
- explosion-vented vessel /
- venting duct /

HTML全文

图 1 导管泄爆容器压力峰值

Figure 1. Peak pressures in vessel vented by duct

下载: 全尺寸图片幻灯片

表 1 容器带导管泄爆实验数据

Table 1. The experimental values for vessel venting by duct

No.	K_G/(MPa·m·s^-1)	φ/%	点火位置	L_t/m	D_t/m	V/m³	p_v/kPa	p₀/kPa	p_red/kPa
1	10.0	4	1	0.60	0.016	0.003 66	101	101	145
2	10.0	4	1	0.60	0.021	0.003 66	101	101	117
3	10.0	4	1	0.60	0.036	0.003 6	101	101	127
4	10.0	4	1	1.10	0.016	0.003 66	101	101	180
5	10.0	4	1	1.10	0.021	0.003 66	101	101	145
6	10.0	4	1	1.10	0.036	0.003 66	101	101	192
7	10.0	4	1	2.60	0.016	0.003 66	101	101	192
8	10.0	4	1	2.60	0.021	0.003 66	101	101	155
9	10.0	4	1	2.60	0.036	0.003 66	101	101	192
10	10.0	4	1	2.60	0.053	0.003 66	101	101	211
11	10.0	4	2	1.70	0.036	0.003 66	101	101	201
12	10.0	4	2	1.70	0.036	0.003 66	131	101	216
13	10.0	4	2	1.70	0.036	0.003 66	192	101	266
14	10.0	4	2	1.70	0.036	0.003 66	331	101	337
15	10.0	4	1	1.70	0.036	0.003 66	101	101	176
16	10.0	4	1	1.70	0.036	0.003 66	133	101	188
17	10.0	4	1	1.70	0.036	0.003 66	184	101	181
18	10.0	4	3	1.70	0.036	0.003 66	212	101	127
19	10.0	4	3	1.70	0.036	0.003 66	325	101	224
20	10.0	5	2	1.00	0.844 6	2.600	111	101	19
21	10.0	5	2	2.00	0.844 6	2.600	111	101	30
22	10.0	5	2	3.00	0.844 6	2.600	111	101	39
23	10.0	5	1	3.00	0.844 6	2.600	111	101	101
24	8.4	5	2	25.00	0.500	10.000	111	101	410
25	8.4	5	2	25.00	0.500	10.000	106	101	280
26	8.4	5	2	4.00	0.200	2.000	116	101	430
27	8.4	5	2	10.00	0.200	2.000	116	101	520
28	8.4	5	2	10.00	0.380	2.000	116	101	215
29	8.4	5	2	1.83	0.050	0.027	121	101	500
30	8.4	5	2	2.35	0.050	0.027	126	101	440
31	8.4	5	2	2.35	0.050	0.027	126	101	350
32	8.4	5	2	2.35	0.050	0.027	266	101	190
33	8.4	5	2	1.83	0.050	0.027	243	101	440
34	14.0	18	2	0.16	0.035	0.022	101	101	300
35	14.0	18	2	0.32	0.035	0.022	101	101	482
36	14.0	18	2	0.54	0.035	0.022	101	101	565
37	14.0	18	2	0.80	0.035	0.022	101	101	482
38	14.0	18	2	1.40	0.035	0.022	101	101	513
39	14.0	18	2	1.75	0.035	0.022	101	101	518
40	14.0	18	2	2.80	0.035	0.022	101	101	214
41	14.0	18	2	3.50	0.035	0.022	101	101	464
42	14.0	18	2	4.91	0.035	0.022	101	101	357
43	14.0	18	2	6.14	0.035	0.022	101	101	375
44	14.0	18	2	6.75	0.035	0.022	101	101	339
45	14.0	18	2	2.50	0.025	0.022	101	101	500
46	14.0	18	2	2.50	0.025	0.022	101	101	473
47	14.0	18	2	2.50	0.025	0.022	101	101	420
48	14.0	10	2	2.50	0.025	0.020	101	101	82
49	14.0	12	2	2.50	0.025	0.020	101	101	238
50	14.0	14	2	2.50	0.025	0.020	101	101	291
51	14.0	16	2	2.50	0.025	0.020	101	101	347
52	14.0	18	2	2.50	0.025	0.020	101	101	400
53	14.0	20	2	2.50	0.025	0.020	101	101	430
54	14.0	22	2	2.50	0.025	0.020	101	101	482
55	14.0	25	2	2.50	0.025	0.020	101	101	500
56	14.0	30	2	2.50	0.025	0.020	101	101	82
57	14.0	20	2	0.04	0.025	0.020	101	101	368
58	14.0	20	2	0.17	0.025	0.020	101	101	368
59	14.0	20	2	0.30	0.025	0.020	101	101	671
60	14.0	20	2	0.61	0.025	0.020	101	101	636
61	14.0	20	2	1.26	0.025	0.020	101	101	457
62	14.0	20	2	2.50	0.025	0.020	101	101	400

下载: 导出CSV

表 2 泄爆压力峰值的SVM检验样本参数

Table 2. Prediction samples for vessel vented through duct

No.	K_G/ (MPa·m·s^-1)	φ/%	点火位置	L_t/m	D_t/m	V/m³	p_v/kPa	p₀/kPa	p_red/kPa
1	10.0	4	1	0.60	0.036	0.003 6	101	101	127
2	10.0	4	1	2.60	0.036	0.003 66	101	101	192
3	10.0	5	2	3.00	0.844 6	2.600	111	101	39
4	14.0	18	2	1.40	0.035	0.022	101	101	513
5	14.0	14	2	2.50	0.025	0.020	101	101	291
6	14.0	18	2	6.75	0.035	0.022	101	101	339
7	10.0	4	2	1.70	0.036	0.003 66	192	101	266
8	14.0	18	2	2.50	0.025	0.022	101	101	420
9	10.0	4	1	1.10	0.021	0.003 66	101	101	145
10	14.0	18	2	2.50	0.025	0.022	101	101	473

下载: 导出CSV

表 3 泄爆压力峰值预测值与检验样本值的对比

Table 3. Predicted values of peak pressure in vessel vented by duct

No.	p_red/kPa	经验公式			支持向量机
No.	p_red/kPa	p/kPa	Δp/kPa	ε/%	p/kPa	Δp/kPa	ε/%
1	127	278	151	118.90	155.6	28.6	22.52
2	192	410	218	113.54	187.7	-4.3	2.24
3	39	31.5	-7.5	19.23	45.5	6.5	16.67
4	513	450	-63	12.28	450.8	-62.2	12.13
5	291	480	189	64.95	295.0	4.0	1.37
6	339	534	195	57.52	333.5	-5.5	1.62
7	266	920	654	245.86	285.5	19.5	7.33
8	420	440	20	4.76	435.7	15.7	3.74
9	145	541	396	273.10	154.5	9.5	6.55
10	473	543	70	14.80	435.3	-37.7	7.97

下载: 导出CSV

参考文献(16)

[1]	American National Standards Institute. NFPA 68: Standard on explosion protection by deflagration venting[S]. Quincy: National Fire Protection Association, 2007.
[2]	Verein Deutscher Ingenieure. VDI 3673, PartⅠ: Pressure venting of dust explosions[S]. Dusseldorf: Verien Deutscher Ingenieure, 1995.
[3]	师喜林, 王志荣, 蒋军成.球形容器内气体的泄爆过程[J].爆炸与冲击, 2009, 29(4): 390-394. Shi Xi-lin, Wang Zhi-rong, Jiang Jun-cheng. Explosion-vented processes for methane-air premixed gas in spherical vessels with venting pipes[J]. Explosion and Shock Waves, 2009, 29(4): 390-394.
[4]	Ponizy B, Leyer J C. Flame dynamics in a vented vessel connected to a duct: 1. Mechanism of vessel-duct interaction[J]. Combustion and Flame, 1999, 116: 259-271.
[5]	Ponizy B, Leyer J C. Flame dynamics in a vented vessel connected to a duct: 2. Influence of ignition site, membrane rupture, and turbulence[J]. Combustion and Flame, 1999, 116: 272-281.
[6]	Ferrara G, Willacy S K, Phylaktou H N, et al. Venting of gas explosion through relief ducts: Interaction between internal and external explosions[J]. Journal of Hazardous Materials, 2008, 155: 358-368.
[7]	Benedetto A D, Russo P, Salzano E. The design of duct venting of gas explosions[J]. Process Safety Progress, 2008, 27(2): 164-172.
[8]	Bartknecht W, Zwahlen G. Explosionsschutz[J]. Grundlagen und Anwendung, 1993: 470-535.
[9]	Vapnik V N. The nature of statistical learning theory[M]. Springer, 1995.
[10]	钱渊, 宋军, 傅柯.基于支持向量机补偿的灰色模型网络流量预测[J].探测与控制学报, 2012, 34(1): 69-72. Qian Yuan, Song Jun, Fu Ke. Grey model of network traffic prediction based on support vector machines[J]. Journal of Detection and Control, 2012, 34(1): 69-72.
[11]	杜颖, 卢继平, 李青, 等.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术, 2008, 32(15): 62-66. Du Ying, Lu Ji-ping, Li Qing, et al. Short-term wind speed forecasting of wind farm based on least square-support vector machine[J]. Power System Technology, 2008, 32(15): 62-66.
[12]	Esen H, Inalli M, Sengur A, et al. Modeling aground-coupled heat pump system by a support vector machine[J]. Renewable Energy, 2008, 33(8): 1814-1823.
[13]	Pan Yong, Jiang Jiang-jun, Wang Rui, et al. A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine[J]. Journal of Hazardous Materials, 2009, 168: 962-969.
[14]	Cai Jie-jin. Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon[J]. Annals of Nuclear Energy, 2012, 43: 114-122.
[15]	孟倩, 王洪权, 王永胜, 等.煤自燃极限参数的支持向量机预测模型[J].煤炭学报, 2009, 34(11): 1489-1493. Meng Qian, Wang Hong-quan, Wang Yong-sheng, et al. Predicting limit parameters of coal self-ignition based on support vector machine[J]. Journal of China Coal Society, 2009, 34(11): 1489-1493.
[16]	杨斌, 匡立春, 孙中春.一种用于测井油气层综合识别的支持向量机方法[J].测井技术, 2005, 29(6): 511-514. Yang Bin, Kuang Li-chun, Sun Zhong-chun. On support vector machines method to identify oil and gas zone with logging and mudlog information[J]. Well Logging Technology, 2005, 29(6): 511-514.