基于大数据的储罐大修周期预测方法  

Study on prediction method of storage tank overhaul cycle based on big data

作　　者：郝悦 王晶 孙成德 袁文彬 程伟 HAO Yue;WANG Jing;SUN Chengde;YUAN Wenbin;CHENG Wei(Hefei General Machinery Research Institute Co.,Ltd.,Hefei 230031,China;PipeChina Network Corporation Eastern Oil Storage and Transportation Co.,Ltd,Xuzhou 221008,China)

机构地区：[1]合肥通用机械研究院有限公司,安徽合肥230031 [2]国家管网集团东部原油储运有限公司,江苏徐州221008

出　　处：《石油化工腐蚀与防护》2024年第5期32-38,共7页Corrosion & Protection In Petrochemical Industry

基　　金：国家管网集团2021年第四批集团公司级科技项目(WZXGL202107);安徽省重点研究与开发计划(202104a07020011)。

摘　　要：当前国内储运行业的大量储罐面临着长周期运行和安全生产的双重压力,而现行周期性的维修模式存在着“过度检验”和“检验不足”的情况,不利于储罐管理与安全运行。储罐腐蚀环境、失效模式和损伤机理复杂,传统的通过检测和评估来制定大修周期的方法难以考虑其中的耦合关联问题。随着计算机技术的发展和应用,利用大数据技术对储罐设施的运行状况和运行周期进行预测成为可能。针对现有方法的不足,提出了一种基于大数据的储罐大修周期预测方法,并利用实际储罐数据验证该方法的可靠性与有效性,结果显示,约80%的储罐大修周期预测值与真实值相差不足1 a。At present,a large number of storage tanks in domestic storage and transportation industry are facing dual pressure of long-term operation and safe production.However,current periodic maintenance mode has the situations of“excessive inspection”and“insufficient inspection”,which is not conducive to tank management and safe operation.The corrosion environment,failure modes,and damage mechanisms of storage tanks are complicated,so it is difficult to consider coupling relation by traditional method of determining overhaul cycles through detection and evaluation.With the development and application of computer technology,big data make it possible to predict operating status and cycle of storage tank facilities.A prediction method of tank overhaul cycle based on big data was proposed to address the shortcomings of existing methods,and its reliability and effectiveness were verified by actual tank data.The results showed that about 80%of predictive values of tank overhaul cycle were less than one year away from the actual values.

关 键 词：储罐 大修周期预测 大数据 

分 类 号：TE972[石油与天然气工程—石油机械设备]