A Prediction Method Based on Fuzzy Neural Network for Corrosion Rate of Marine Pipelines

doi:10.11902/1005.4537.2014.221

Journal of Chinese Society for Corrosion and protection

2015, Vol. 35

Issue (6): 571-576 DOI: 10.11902/1005.4537.2014.221

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A Prediction Method Based on Fuzzy Neural Network for Corrosion Rate of Marine Pipelines

Zhian DENG(

),Shuyi LI,Xiaokun LI,Shan WANG,Xiaojun WANG

College of Petroleum Engineering, Xi'an Shiyou University, Xi'an 710065, China

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Abstract

An intelligent method based on fuzzy neural network and grey correlation analysis was proposed to predict the corrosion rate of oil and gas pipelines in marine environment. Through correlation analysis, the correlation between the corrosion rate of pipelines and environmental factors was built, and from which then the appropriate factors with high correlation could be picked out. Finally, the mapping relationship between the corrosion rate of pipelines and environmental factors could be figured out through fuzzy neural network. The validity and reliability of the results predicted by the proposed method were tested with statistic data which involved different environmental factors, it follows that the average relative error of the predicted corrosion rate was 5.96%, in other words, the present method exhibited a good accuracy in prediction even that less environmental factors were involved during the analysis process. Therefore, the method based on fuzzy neural network and grey correlation analysis, can predict the corrosion rate of pipelines rapidly and accurately with the known environmental factors.

Key words: fuzzy neural network pipeline corrosion prediction grey correlation analysis

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Cite this article:

Zhian DENG,Shuyi LI,Xiaokun LI,Shan WANG,Xiaojun WANG. A Prediction Method Based on Fuzzy Neural Network for Corrosion Rate of Marine Pipelines. Journal of Chinese Society for Corrosion and protection, 2015, 35(6): 571-576.

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https://www.jcscp.org/EN/10.11902/1005.4537.2014.221 OR https://www.jcscp.org/EN/Y2015/V35/I6/571

Fig.1 Topology of T-S FNN

Fig.2 Experimental procedure

Serial number	Oceantemperature / ℃	Dissolved oxygen / mg·L^-1	Salinity10³ mg·L^-1	pH	Redox potential / mV	Corrosion rateμA·cm^-2
1	25.90	6.71	30.10	5.10	378	16.40
2	29.35	6.09	29.00	6.30	400	16.90
3	27.90	6.18	31.50	7.00	363	15.57
4	24.00	7.95	30.20	8.10	324	13.65
5	28.00	5.05	31.40	9.20	240	13.24
6	27.32	3.21	29.31	8.20	281	12.91
7	27.87	6.55	31.68	7.20	356	14.06
8	28.27	6.98	28.20	6.60	384	15.47
9	30.70	7.15	31.74	6.50	401	16.28
10	29.37	6.82	30.12	6.20	414	17.11
11	24.27	0.80	32.56	8.10	717	3.61
12	27.45	2.60	35.37	7.96	287	7.94
13	27.23	4.20	31.94	7.89	289	9.63
14	27.48	5.90	32.39	7.83	331	10.58
15	28.75	6.80	32.22	8.00	340	11.43
16	28.52	8.40	32.10	8.01	345	12.52
17	28.45	9.90	31.95	7.93	309	22.64
18	23.95	7.61	9.17	8.04	231	10.94
19	24.73	6.06	17.33	7.88	321	11.45
20	24.60	7.52	24.42	7.57	210	11.83
21	24.51	7.02	32.00	8.16	308	12.55
22	23.65	6.51	41.34	7.67	245	8.40
23	16.74	7.11	33.55	8.25	178	10.85
24	21.11	6.03	33.44	8.03	295	11.45
25	25.57	6.70	32.19	8.09	325	11.87
26	31.16	4.38	33.21	7.94	242	8.92
27	24.27	0.80	32.56	8.10	171	2.55
28	27.45	2.60	35.37	7.96	287	10.96
29	27.23	4.20	31.94	7.89	289	12.00
30	28.72	6.80	32.21	8.00	325	13.33
31	28.52	8.40	32.10	8.01	345	17.31
32	28.45	9.90	31.95	7.93	309	22.48
33	23.95	7.61	9.17	8.04	231	8.13
34	24.95	6.80	16.29	7.82	341	9.07
35	24.60	7.52	24.42	7.57	210	10.74
36	27.32	3.12	29.31	8.20	281	13.59
37	24.00	7.95	30.20	8.10	324	12.89
38	27.78	6.35	31.38	7.20	356	13.61
39	27.97	6.05	31.94	6.60	384	14.60
40	30.70	7.15	31.74	6.50	401	15.00
41	29.37	6.82	30.12	6.20	414	15.39
42	29.35	6.09	29.00	6.30	400	16.45
43	27.00	6.70	30.70	7.00	350	12.60
44	27.90	5.15	31.50	9.20	264	9.08
45	25.55	6.67	31.00	8.09	320	12.49
46	24.31	6.42	40.67	7.88	250	8.75
47	24.11	6.38	41.00	7.98	228	8.99
48	17.45	7.48	34.08	8.10	135	17.05
49	21.95	8.28	34.64	7.95	113	17.34
50	27.19	4.91	33.50	7.99	275	15.48

Table 1 Test data of marine corrosion^[1]

Fig.3 Correlation of influencing factors

Table 2 Comparisons of the results predicted by FNN in this work and BP network in Ref.[1]

Fig.4 Comparisons of the results obtained by FNN andpractical testing

Table 3 Comparisons of various parameters in prediction testing

Fig.5 Comparisons of the relative error of test results

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