| 引用本文: | 肖荣鸽,靳帅帅.基于WOA-BP算法的海底管道腐蚀速率预测[J].海洋科学,2022,46(6):116-123. |
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| 基于WOA-BP算法的海底管道腐蚀速率预测 |
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肖荣鸽, 靳帅帅
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西安石油大学 石油工程学院 陕西省油气田特种增产技术重点实验室, 陕西 西安 710065
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| 摘要: |
| 管道腐蚀速率的影响因素众多,构成了异常复杂的腐蚀体系,很难对其进行准确预测。针对单一BP模型初始权值和阈值的选取不当容易陷入局部最优等问题,引入WOA算法优化BP神经网络对海底管道腐蚀速率进行预测,并与GA和PSO算法优化BP预测模型进行对比,验证WOA-BP模型的预测效果和可行性。结果表明:WOA-BP模型的平均绝对百分误差和均方根误差分别为3.689%和0.1537,远低于单一BP、PSO-BP、GA-BP模型,具有较高的预测精度和稳定性,可以为海底管道内腐蚀防护和油气管道流动保障提供决策支持。 |
| 关键词: 腐蚀速率 WOA算法 BP模型 GA算法 PSO算法 |
| DOI：10.11759/hykx20210823003 |
| 分类号:TG174 |
| 基金项目:陕西省教育厅2019年度服务地方专项计划项目(19JC034) |
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| Prediction of the submarine pipeline corrosion rate based on the whale optimization algorithm and back propagation (WOA-BP) algorithm |
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XIAO Rong-ge, JIN Shuai-shuai
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Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil & Gas Reservoirs, College of Petroleum Engineering, Xi'an Shiyou University, Xi'an 710065, China
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| Abstract: |
| Many factors affect the pipeline corrosion rate, constituting a highly complex corrosion system; thus, accurately predicting the pipeline corrosion rate is difficult. A single back propagation (BP) model can easily fall into the local optimum due to an improper selection of the initial weight and threshold value. To address this problem, the whale optimization algorithm (WOA) algorithm is introduced for BP neural network optimization to predict the corrosion rate of a submarine pipeline. Then, it is compared with the GA and PSO algorithms to optimize the BP prediction model to verify the prediction effect and feasibility of the WOA-BP model. The results show that the average absolute percentage and root mean square errors of the WOA-BP model are 3.689% and 0.1537, respectively, considerably lower than those of the single BP, PSO-BP, and GA-BP models. It has high prediction accuracy and stability and can provide decision support for corrosion protection in the submarine pipeline and flow guarantee of the oil and gas pipeline. |
| Key words: corrosion rate WOA algorithm BP model GA algorithm PSO algorithm |
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