基于物理信息神经网络的油气管道内腐蚀预测方法

周涛涛, 刘迎正, 郑文培, 姜恒良, 刘海鹏, 夏刚

Pipeline Corrosion Prediction Method Based on Physics-informed Neural Networks

ZHOU Taotao, LIU Yingzheng, ZHENG Wenpei, JIANG Hengliang, LIU Haipeng, XIA Gang

表1 管道内腐蚀预测模型的相关变量

Table 1 Variables in prediction models of corrosion of pipelines

Labels	Variable	Variable name	Unit	Range	Average
X₁	T	Temperature	℃	41.7-69.4	58.5
X₂	P	System pressure	Pa	1.52 × 10⁶-3.23 × 10⁶	2.28 × 10⁶
X₃	$p_{C O_{2}}$	CO₂ partial pressure	Pa	2.9 × 10⁴-3.8 × 10⁴	3.3 × 10⁴
X₄	pH	pH	-	4.2-6.2	5.8
X₅	Vf	Fluid flow rate	m·s^-1	0.37-0.69	0.53
X₆	Cl^-	Cl^- concentration	mg·L^-1	3420-8280	6227
X₇	CO₂ eq	CO₂ concentration	mg·L^-1	15.8-27.4	22.7
X₈	HCO $_{3}^{-}$	HCO $_{3}^{-}$ concentration	mg·L^-1	107-208	150
X₉	WC	Water content	%	52.5-63.9	58.6
Y	V_corr	Internal corrosion rate	mm·a^-1	2.289-3.105	2.681