Prediction of external corrosion rate of offshore oil and gas pipelines based on FA-BAS-ELM

doi:10.16265/j.cnki.issn1003-3033.2022.02.014

Abstract

Abstract:

In order to improve prediction accuracy and efficiency of external corrosion rate of submarine oil and gas pipelines, a ELM prediction model based on FA and BAS was established. FA method was used to reduce dimension of influencing factors' data set and determine input variables of ELM network. Then, an ELM prediction model was established, and its parameters were optimized by BAS to avoid influence of randomness of parameter values on its predictive performance. Finally, with the real sea hanging film experiment as an example, predictive performance of the model was evaluated through modeling and simulation, and it was compared with other models. The results show that the mean absolute percentage error (MAPE) of FA-BAS-ELM prediction model is only 1.92, and its determination coefficient R² reaches as high as 0.994 9, indicating that the model has better prediction accuracy and performance.

Key words: submarine oil and gas pipeline, external corrosion rate, factor analysis (FA), beetle antennae search algorithm (BAS), extreme learning machine (ELM)

ZHANG Xinsheng, CHANG Yingge. Prediction of external corrosion rate of offshore oil and gas pipelines based on FA-BAS-ELM[J]. China Safety Science Journal, 2022, 32(2): 99-106.

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序号	V₁/℃	V₂/(mg·L^-1)	V₃/(10³mg·L^-1)	V₄	V₅/mV	V₆/(μA·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
︙	︙	︙	︙	︙	︙	︙
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

成分	初始特征值			旋转载荷平方和
成分	特征值	方差比/%	累积比/%	特征值	方差比/%	累计比/%
1	1.849	36.989	36.989	1.389	27.789	27.789
2	1.245	24.898	61.887	1.09	21.807	49.596
3	0.871	17.428	79.315	1.075	21.494	71.09
4	0.59	11.808	91.123	1.002	20.033	91.123
5	0.444	8.877	100	—

变量	旋转前				旋转后
变量	F₁	F₂	F₃	F₄	F₁	F₂	F₃	F₄
V₄	0.828	0.112	-0.182	-0.209	-0.894	-0.24	-0.106	-0.026
V₅	0.777	0.091	-0.03	0.606	0.73	-0.316	0.371	-0.064
V₂	-0.744	0.327	-0.248	0.382	0.044	0.961	-0.027	-0.105
V₁	-0.071	-0.821	0.475	0.18	0.235	-0.018	0.961	0.032
V₃	0.031	0.666	0.742	-0.014	-0.011	-0.097	0.024	0.992

变量	F₁	F₂	F₃	F₄
V₁	-0.287	0.125	1.068	0.006
V₂	-0.025	0.902	0.102	0.045
V₃	0.02	0.067	-0.014	1.003
V₄	-0.773	-0.18	0.306	-0.092
V₅	0.518	-0.314	0.024	-0.102

序号	F₁	F₂	F₃	F₄
1	2.933 4	0.569 0	-1.110 6	0.170 7
2	1.541 7	0.060 8	0.571 9	-0.182 3
3	0.788 4	0.033 6	0.315 9	0.182 3
4	-0.170 6	0.563 4	-0.563 9	-0.081 7
5	-2.071 7	-0.581 1	1.122 2	0.016 5
︙	︙	︙	︙	︙
46	-0.342 8	0.275 4	-0.664 1	1.694 9
47	-0.544 0	0.303 5	-0.705 3	1.759 9
48	-0.578 9	0.752 0	-2.995 2	0.735 8
49	-0.995 1	1.430 3	-1.405 6	0.896 1
50	-0.592 1	-0.498 1	0.354 3	0.457 5