|本期目录/Table of Contents|

[1]骆正山,刘月.盐湖大气环境下316 L仪表管点蚀深度预测研究*[J].中国安全生产科学技术,2024,20(6):105-110.[doi:10.11731/j.issn.1673-193x.2024.06.014]
　LUO Zhengshan,LIU Yue.Prediction on pitting depth of 316 L instrument tube in salt-lake atmospheric environment[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(6):105-110.[doi:10.11731/j.issn.1673-193x.2024.06.014]
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盐湖大气环境下316 L仪表管点蚀深度预测研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年6期
页码:
105-110
栏目:
职业安全卫生管理与技术
出版日期:
2024-06-30

文章信息/Info

Title:
Prediction on pitting depth of 316 L instrument tube in salt-lake atmospheric environment
文章编号:
1673-193X(2024)-06-0105-06
作者:
骆正山; 刘月
（西安建筑科技大学 管理学院，陕西 西安 710055)
Author(s):
LUO Zhengshan;  LIU Yue
(School of Management,Xi’an University of Architecture and Technology,Xi’an Shaanxi 710055,China)
关键词:
盐湖大气环境; 316 L仪表管; 点蚀深度; 改进黏菌算法(ISMA); FGM（1; 1; r）模型
Keywords:
salt-lake atmospheric environment;  316 L instrument tube;  pitting depth;  improved slime mold algorithm (ISMA);  FGM (1; 1; r) model
分类号:
X937
DOI:
10.11731/j.issn.1673-193x.2024.06.014
文献标志码:
A
摘要:
为提高316 L仪表管在盐湖大气环境下点蚀深度的预测精度，采用变阶平均弱化缓冲算子、积分背景值和新陈代谢对分数阶累加灰色模型FGM（1,1,r）进行改进,首先通过改进Tent混沌映射、莱维飞行和区间自适应反向学习策略提高黏菌算法（SMA）的寻优能力和收敛速度，随后利用改进黏菌算法（ISMA）对FGM（1,1,r,ρ）中的参数r和ρ进行寻优，最后构建仪表管ISMA-FGM（1,1,r,ρ）点蚀深度预测模型。研究结果表明：经优化的新模型比原模型误差更小、拟合度更高，在仪表管点蚀深度预测方面具有更好的性能。研究结果可为仪表管道系统的完整性评价和风险预警提供参考。
Abstract:
In order to improve the prediction accuracy of pitting depth by the 316 L instrument tube in the salt-lake atmospheric environment,the fractional order cumulative grey model (FGM(1,1,r)) was improved by using the variable order average weakening buffer operator,integrated background value and metabolism.Firstly,the optimization ability and convergence speed of slime mould algorithm (SMA) were improved by improving the Tent chaos mapping,Levy flight and interval adaptive reverse learning strategies.Then,the parameters r and ρ in FGM(1,1,r,ρ) were optimized by ISMA.Finally,the ISMA-FGM (1,1,r,ρ) prediction model of the pitting depth of instrument tube was constructed.The results show that the optimized new model has smaller error and higher fitting degree than the original model,and has better performance in predicting the pitting depth of instrument tube.The research results can provide a reference for the integrity evaluation and risk warning of instrument tube system.

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备注/Memo

备注/Memo:
收稿日期： 2023-04-21；网络首发日期： 2024-06-07
* 基金项目： 国家自然科学基金项目(41877527)
作者简介： 骆正山，博士，教授，主要研究方向为管理科学与工程、信息管理与信息系统、油气管道风险评估等。
通信作者： 刘月，硕士研究生，主要研究方向为仪表管道风险评估、建模与预测。
更新日期/Last Update: 2024-06-25