凌晓,徐鲁帅,高甲程,马娟娟,马贺清,付小华.基于IFA-BPNN的长输管道外腐蚀速率预测[J].表面技术,2021,50(4):285-293.
LING Xiao,XU Lu-shuai,GAO Jia-cheng,MA Juan-juan,MA He-qing,FU Xiao-hua.Prediction of External Corrosion Rate of Oil Pipeline Based on Improved IFA-BPNN[J].Surface Technology,2021,50(4):285-293
基于IFA-BPNN的长输管道外腐蚀速率预测
Prediction of External Corrosion Rate of Oil Pipeline Based on Improved IFA-BPNN
投稿时间:2020-07-30  修订日期:2020-11-27
DOI:10.16490/j.cnki.issn.1001-3660.2021.04.029
中文关键词:  萤火虫算法  BP神经网络  混沌初始化  惯性权重  管道  腐蚀速率预测
英文关键词:firefly algorithm  BP neural network  chaos initialization  inertia weight  oil pipelines  corrosion rate prediction
基金项目:国家自然科学基金青年项目(51904138);甘肃省自然科学基金(20JR5RA451);甘肃省高等学校创新能力提升项目(2020A-019)
作者单位
凌晓 兰州理工大学 石油化工学院,兰州 730050 
徐鲁帅 兰州理工大学 石油化工学院,兰州 730050 
高甲程 中国石油天然气股份有限公司 甘肃兰州销售分公司,兰州 730050 
马娟娟 兰州理工大学 石油化工学院,兰州 730050 
马贺清 兰州理工大学 石油化工学院,兰州 730050 
付小华 兰州理工大学 理学院,兰州 730050 
AuthorInstitution
LING Xiao College of Petroleum and Chemical Engineering, Lanzhou 730050, China 
XU Lu-shuai College of Petroleum and Chemical Engineering, Lanzhou 730050, China 
GAO Jia-cheng PetroChina Gansu Lanzhou Marketing Company, Lanzhou 730050, China 
MA Juan-juan College of Petroleum and Chemical Engineering, Lanzhou 730050, China 
MA He-qing College of Petroleum and Chemical Engineering, Lanzhou 730050, China 
FU Xiao-hua College of Sciences, Lanzhou University of Technology, Lanzhou 730050, China 
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中文摘要:
      目的 构建陆地长输管道外腐蚀速率的预测模型,提升管道外腐蚀速率预测的精度,对长输管道外腐蚀状态进行准确把控。方法 深入解析了萤火虫算法(FA)的工作原理,针对FA易出现陷入局部最优或因控制参数设置不合适而导致函数无法收敛等问题,提出了FA的改进方案:采用Logistics混沌映射的方法初始化萤火虫的位置,提升萤火虫种群的所养性;引入一种新的惯性权重计算方法来改进萤火虫位置移动公式,提升FA全局寻优能力。利用改进的萤火虫算法(IFA)对误差反向传播神经网络(BPNN)初始权值和阈值进行优化,建立基于IFA-BPNN的长输管道外腐蚀速率预测模型。以111组长输管道外腐蚀检测数据为例,在MATLAB中进行模拟仿真计算,使用粒子群算法优化的BPNN(PSO-BPNN)、遗传算法优化的BPNN(GA-BPNN)以及未进行优化的BPNN作为对比模型进行对比分析。结果 使用IFA优化BPNN,大幅提升了BPNN模型的预测精度。使用IFA-BPNN模型预测12组管道腐蚀速率,平均相对误差仅为5.94%,预测结果的R2为0.995 95,均优于BPNN、PSO-BPNN以及GA-BPNN模型的预测结果。结论 IFA-BPNN作为预测管道腐蚀速率工具具有较好的预测精度和鲁棒性。
英文摘要:
      In order to establish a machine learning model for predicting the external corrosion rate of long land transport pipelines, improve the prediction accuracy of the external corrosion rate of the pipeline, and accurately grasp the external corrosion status of the long-distance pipeline, this paper analyzes the working principle of FA, to solve the problems of FA, such as local optimization or function convergence failure due to initial parameter setting, and an improved FA algorithm is proposed:This paper uses the method of Logistics chaotic mapping to initialize the position of the firefly, and improve the cultivability of the firefly population; this paper introduces a new inertia weight calculation method to improve the formula of the firefly position movement and enhance the FA global optimization ability. The improved FA (IFA) was used to optimize the initial weights and thresholds of BPNN, and a long-distance pipeline external corrosion rate prediction model based on IFA-BPNN was established. Taking 111 sets of long-distance pipeline external corrosion detection data as an example, the simulation calculation is carried out in MATLAB, and PSO-BPNN, GA-BPNN and unoptimized BPNN are used as comparative models for comparative analysis. The IFA model is used to initialize the BPNN model, which greatly improves the prediction accuracy of the BPNN model. The IFA-BPNN model was used to predict and analyze the external corrosion rates of 12 groups of pipelines, the average relative error was only 5.94%, and the R2 of the prediction results was 0.995 95. The prediction results of IFA-BPNN model are superior to those of BPNN model, PSO-BPNN model and GA-BPNN model in all aspects. IFA-BPNN has good accuracy and robustness as a tool to predict pipeline corrosion rate.
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