[1]肖述辉,杜传甲,王成军.基于改进麻雀搜索算法优化BP神经网络的管道腐蚀速率预测模型[J].油气储运,2024,43(07):1-13.
 XIAO Shuhui,Du Chuanjia,WANG Chengjun.Pipeline corrosion rate prediction using BP neural network based on improved sparrow search algorithm[J].Oil & Gas Storage and Transportation,2024,43(07):1-13.
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基于改进麻雀搜索算法优化BP神经网络的管道腐蚀速率预测模型

参考文献/References:

[1] 罗鹏,张一玲,蔡陪陪,郭正虹,陈洪源,王维斌.长输天然气管道内腐蚀事故调查分析与对策[J].全面腐蚀控制,2010,24(6):16-21. DOI:10.3969/j.issn.1008-7818.2010.06.009.
LUO P, ZHANG Y L, CAI P P, GUO Z H, CHEN H Y, WANG W B. Analysis and countermeasures of natural gas transmission pipeline internal corrosion accidents[J]. Total Corrosion Control, 2010, 24(6): 16-21.
[2] GONG Y H, LI Y T. STAMP-based causal analysis of China-Donghuang oil transportation pipeline leakage and explosion accident[J]. Journal of Loss Prevention in the Process Industries, 2018, 56: 402-413. DOI: 10.1016/j.jlp.2018.10.001.
[3] 吕林林,王杰,祁庆芳,郭策,贺蓉蓉,孙小伟.基于KPCA-IGOA-ELM的油气混输管道腐蚀速率预测模型[J].油气储运,2023,42(7):785-792. DOI:10.6047/j.issn.1000-8241.2023.07.007.
LYU L L, WANG J, QI Q F, GUO C, HE R R, SUN X W. Corrosion rate prediction model of oil-gas mixed transportation pipelines based on KPCA-IGOA-ELM[J]. Oil & Gas Storage and Transportation, 2023, 42(7): 785-792.
[4] 宋成立.多因素作用下油气集输管道的腐蚀行为及预测模型[D].西安:西北大学,2022.
SONG C L. Corrosion behavior and prediction model of oil and gas gathering pipelines under multi-factors[D]. Xi’an: Northwest University, 2022.
[5] ZHI Y J, FU D M, YANG T, ZHANG D W, LI X G, PEI Z B. Long-term prediction on atmospheric corrosion data series of carbon steel in China based on NGBM(1,1) model and genetic algorithm[J]. Anti-Corrosion Methods and Materials, 2019, 66(4): 403-411. DOI: 10.1108/ACMM-11-2017-1858.
[6] 巴振宁,韩亚鑫,梁建文.基于改进AHP和模糊综合评价法的燃气管道腐蚀风险评价[J].安全与环境学报,2018,18(6):2103-2109. DOI:10.13637/j.issn.1009-6094.2018.06.009.
BA Z N, HAN Y X, LIANG J W. Risk assessment of the gas pipeline corrosion based on the improved AHP and fuzzy comprehensive evaluation method[J]. Journal of Safety and Environment, 2018, 18(6): 2103-2109.
 [7] PAUL S, MONDAL R. Computation of corrosion rates of steel with variation of Cl-, SO42-, CO32-, HCO3-, O2, CO2, pH and temperature, developed by modeling with artificial neural network[J]. Innovations in Corrosion and Materials Science, 2017, 7(2): 135-143. DOI: 10.2174/2352094907666171016124420.
[8] 骆正山,陈晨,王哲.优化的Gray Markov模型在埋地管道腐蚀速率预测中的应用[J].腐蚀与防护,2019,40(5):313-317,326. DOI:10.11973/fsyfh-201905001.
LUO Z S, CHEN C, WANG Z. Application of improved Gray Markov dynamic model in predicting corrosion rates of oil and gas pipelines[J]. Corrosion and Protection, 2019, 40(5): 313-317, 326.
[9] 吴奇兵,张士超,曹义威,安广山,屈会智,陈泽光,等.基于模糊综合评价法的海上采油树安全分级[J].石油机械,2021,49(10):65-70. DOI:10.16082/j.cnki.issn.1001-4578.2021.10.010.
WU Q B, ZHANG S C, CAO Y W, AN G S, QU H Z, CHEN Z G, et al. Safety classification of offshore Christmas tree based on fuzzy comprehensive evaluation method[J]. China Petroleum Machinery, 2021, 49(10): 65-70.
[10] ROCABRUNO-VALDÉS C I, GONZÁLEZ-RODRIGUEZ J G, DÍAZ-BLANCO Y, JUANTORENA A U, MUÑOZ-LEDO J A, EL-HAMZAOUI Y, et al. Corrosion rate prediction for metals in biodiesel using artificial neural networks[J]. Renewable Energy, 2019, 140: 592-601. DOI: 10.1016/j.renene.2019.03.065.
[11] 陈艳,康伟杰,姚铭,董彩常.基于FLUENT和神经网络预测海水弯管冲刷腐蚀的模型[J].腐蚀与防护,2019,40(6):436-440. DOI:10.11973/fsyfh-201906009.
CHEN Y, KANG W J, YAO M, DONG C C. Prediction model for erosion-corrosion of seawater bend based on fluent and neural network[J]. Corrosion and Protection, 2019, 40(6): 436-440.
[12] 胡松青,石鑫,胡建春,任振甲,郭爱玲,高元军.基于BP神经网络的输油管道内腐蚀速率预测模型[J].油气储运,2010,29(6):448-450. DOI:10.6047/j.issn.1000-8241.2010.06.014.
HU S Q, SHI X, HU J C, REN Z J, GUO A L, GAO Y J. BP neural network-based prediction model for internal corrosion rate of oil pipelines[J]. Oil & Gas Storage and Transportation, 2010, 29(6): 448-450.
[13] HU Q F, LIU Y C, ZHANG T, GENG S J, WANG F H. Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network[J]. Journal of Materials Science & Technology, 2019, 35(1): 168-175. DOI: 10.1016/j.jmst.2018.06.017.
[14] 朱庆杰,张建龙,陈艳华,赵炫皓,万永华.常州市燃气管网破坏的人工神经网络预测模型[J].工业安全与环保,2020,46(2):44-48. DOI:10.3969/j.issn.1001-425X.2020.02.011.
ZHU Q J, ZHANG J L, CHEN Y H, ZHAO X H, WAN Y H. Artificial network prediction model for gas pipeline network failure in Changzhou city[J]. Industrial Safety and Environmental Protection, 2020, 46(2): 44-48.
[15] 凌晓,徐鲁帅,余建平,梁瑞.基于改进的BP神经网络的输油管道内腐蚀速率预测[J].传感器与微系统,2021,40(2):124-127. DOI:10.13873/J.1000-9787(2021)02-0124-04.
LING X, XU L S, YU J P, LIANG R. Prediction of corrosion rate in oil pipeline based on improved BP neural network[J]. Transducer and Microsystem Technologies, 2021, 40(2): 124-127.
[16] 肖荣鸽,王栋,王勤学.基于ASO-BP神经网络的海底油气管道腐蚀速率预测[J].化学工业与工程,2022,39(6):109-116. DOI:10.13353/j.issn.1004.9533.20216004.
XIAO R G, WANG D, WANG Q X. Prediction of corrosion rate of submarine oil and gas pipelines based on ASO-BP neural network[J]. Chemical Industry and Engineering, 2022, 39(6): 109-116.
[17] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536. DOI: 10.1038/323533a0.
[18] RUMELHART D E, MCCLELLAND J L, CORPORATE PDP Research Group. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations[M]. Cambridge: MIT Press, 1986: 1-547.
[19] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, SUTSKEVER I, SALAKHUTDINOV R. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958. DOI: 10.5555/2627435.2670313.
[20] 焦李成,杨淑媛,刘芳,王士刚,冯志玺.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716. DOI:10.11897/SP.J.1016.2016.01697.
JIAO L C, YANG S Y, LIU F, WANG S G, FENG Z X. Seventy years beyond neural networks: retrospect and prospect[J]. Chinese Journal of Computers, 2016, 39(8): 1697-1716.
[21] XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34. DOI: 10.1080/21642583.2019.1708830.
[22] ZHANG J N, XIA K W, HE Z P, YIN Z X, WANG S J. Semi-supervised ensemble classifier with improved sparrow search algorithm and its application in pulmonary nodule detection[J]. Mathematical Problems in Engineering, 2021, 2021: 6622935. DOI: 10.1155/2021/6622935.
[23] REN C Y, QIAO W, TIAN X. Natural gas pipeline corrosion rate prediction model based on BP Neural network[C]. Singapore: Fuzzy Engineering and Operations Research, 2012: 449-455.
[24] 郑度奎,胡晨章,蒲月华.基于人工神经网络的油气管道CO2腐蚀速率预测研究进展[J].热加工工艺,2021,50(18):25-31. DOI:10.14158/j.cnki.1001-3814.20202208.
ZHENG D K, HU C Z, PU Y H. Research progress in prediction of CO2 corrosion rate of oil and gas pipeline based on artificial neural network[J]. Hot Working Technology, 2021, 50(18): 25-31.
[25] 钱敏,黄海松,范青松.基于反向策略的混沌麻雀搜索算法[J].计算机仿真,2022,39(8):333-339,487. DOI:10.3969/j.issn.1006-9348.2022.08.065.
QIAN M, HUANG H S, FAN Q S. Chaotic sparrow search algorithm based on opposition-based strategy[J]. Computer Simulation, 2022, 39(8): 333-339, 487.
[26] TIZHOOSH H R. Opposition-based learning: a new scheme for machine intelligence[C]. Vienna: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2005: 695-701.
[27] 李爱莲,全凌翔,崔桂梅,解韶峰.融合正余弦和柯西变异的麻雀搜索算法[J].计算机工程与应用,2022,58(3):91-99. DOI:10.3778/j.issn.1002-8331.2106-0148.
LI A L, QUAN L X, CUI G M, XIE S F. Sparrow search algorithm combining sine-cosine and Cauchy mutation[J]. Computer Engineering and Applications, 2022, 58(3): 91-99.
[28] MIRJALILI S. SCA: A sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133. DOI: 10.1016/j.knosys.2015.12.022.
[29] 雍欣,高岳林,赫亚华,王惠敏.多策略融合的改进萤火虫算法[J].计算机应用,2022,42(12):3847-3855. DOI:10.11772/j.issn.1001-9081.2021101830.
YONG X, GAO Y L, HE Y H, WANG H M. Improved firefly algorithm based on multi-strategy fusion[J]. Journal of Computer Applications, 2022, 42(12): 3847-3855.
[30] RASMUSSEN C E, WILLIAMS C K I. Gaussian processes for machine learning[M]. Cambridge: MIT Press, 2006: 79-102.

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备注/Memo

基金项目:陕西省公益性地质调查项目“陕西省国有自然资源资产管理评价体系研究设计”,202202。
作者简介:肖述辉,男,1982年出生,高级工程师,2014年硕士毕业于西安石油大学机械工程专业,现主要从事油气储运管理方向的研究工作。地址:陕西省延安市宝塔区枣园街道枣园路延长石油公司,716000。电话:15319559459。E-mail:9109509@qq.com
作者:杜传甲,男,1993年出生,2017年硕士毕业于西安建筑科技大学工业工程专业,现主要从事复杂系统决策与分析方向的研究工作。地址:陕西省西安市碑林区西安建筑科技大学雁塔校区,710055。电话:18291862590。E-mail:cjdu@xauat.edu.cn

更新日期/Last Update: 2024-05-17