Prediction of external corrosion rate of oil pipeline based on improved IFA-BPNN

doi:10.16490/j.cnki.issn.1001-3660.2021.04.029

CORC > 兰州理工大学 > 兰州理工大学 > 理学院

	Prediction of external corrosion rate of oil pipeline based on improved IFA-BPNN
	Ling, Xiao 2; Xu, Lu-Shuai 2; Gao, Jia-Cheng 3; Ma, Juan-Juan 2; Ma, He-Qing 2; Fu, Xiao-Hua 1
刊名	Surface Technology
	2021-04-01
卷号	50 期号:4 页码:285-293
关键词	Bioluminescence Corrosion rate Forecasting Genetic algorithms Global optimization Machine learning Mapping MATLAB Particle swarm optimization (PSO) Pipelines Predictive analytics Turing machines Water pipelines Average relative error Comparative analysis Function convergence Long distance pipelines Machine learning models Optimization ability Prediction accuracy Simulation calculation
DOI	10.16490/j.cnki.issn.1001-3660.2021.04.029
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. © 2021, Chongqing Wujiu Periodicals Press. All rights reserved.
语种	中文
出版者	Chongqing Wujiu Periodicals Press
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/150948]
专题	理学院
作者单位	1.College of Sciences, Lanzhou University of Technology, Lanzhou; 730050, China; 2.College of Petroleum and Chemical Engineering, Lanzhou; 730050, China; 3.PetroChina Gansu Lanzhou Marketing Company, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Ling, Xiao,Xu, Lu-Shuai,Gao, Jia-Cheng,et al. Prediction of external corrosion rate of oil pipeline based on improved IFA-BPNN[J]. Surface Technology,2021,50(4):285-293.
APA	Ling, Xiao,Xu, Lu-Shuai,Gao, Jia-Cheng,Ma, Juan-Juan,Ma, He-Qing,&Fu, Xiao-Hua.(2021).Prediction of external corrosion rate of oil pipeline based on improved IFA-BPNN.Surface Technology,50(4),285-293.
MLA	Ling, Xiao,et al."Prediction of external corrosion rate of oil pipeline based on improved IFA-BPNN".Surface Technology 50.4(2021):285-293.