Methods of Carrying Out the Anticipative Maintenance of Fluid Hydrocarbons Transport Systems

Robert-Gheorghe Vlădescu
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Abstract

Abstract The scientific knowledge, transposed into the engineering practice, requires the collection, by using the most state-of-the-art and complete means of the information necessary for the decision to initiate the most appropriate measures of predictive maintenance. In this context, the information provided as a result of the investigation of the pipelines intended for the transport of fluid hydrocarbons with smart pigging devices (cleaning, calibration, geometric, magnetic flux leakage) refers to those pre-existing in the questionnaire of the pipeline of the inspection operation. The values in the questionnaire are used to evaluate the anomalies in the inspection reports (preliminary and final). A quantitative assessment of anomalies is based on, and limited exclusively to the results of the inspection, and does not include any numerical parameters (corrosion growth rates, anodic potential etc.), other than those from In-Line Inspection such as values Estimated Repair Factor (ERF) of anomalies. The questionnaire (initial data provided) of the pipeline to be investigated with smart pigging devices includes at least: pipe diameter, wall thickness, pipe material, design pressure, Maximum Allowable Operating Pressure (MAOP), transported product, curve type, investigation history. The detection thresholds are applied in accordance with the manufacturing standards of the pipes. Generally, the calculation results, namely ERF and safe pressure, based on ASME B31G (Manual for Determining the Remaining Strength of Corroded Pipelines) are used to present the pipeline condition. There are several approaches that can be used to characterize the behavior of corrosion anomalies, both pierced and partial. ASME B31G is a very conservative criterion that helps operators avoid unnecessary cuts. It is based on an empirical adequacy to an extensive series of tests on a full scale on vessels with narrow ridges. Depth-based histograms show the distribution of all metal loss characteristics detected along the entire length of the pipe relative to their location and surface. The approach to the referred issue allows the collection of essential information about the pipeline, and presents summaries of any anomalies of the pipeline, having a comprehensive character.
流体烃类输运系统的预期维护方法
摘要将科学知识转化为工程实践,需要使用最先进和最完整的手段收集必要的信息,以便决策发起最适当的预测性维护措施。在这种情况下,对带有智能清管装置的液态碳氢化合物输送管道进行调查(清洁、校准、几何、漏磁)所提供的信息是指检查作业管道问卷中已有的信息。问卷中的值用于评价检验报告中的异常(初检和终检)。异常的定量评估基于且仅限于检查结果,不包括任何数值参数(腐蚀增长率,阳极电位等),除了在线检查的值,如异常的估计修复因子(ERF)。智能清管装置调查管道问卷(提供初步数据)至少包括:管径、管壁厚度、管材、设计压力、最大允许工作压力(MAOP)、输送产品、曲线类型、调查历史。检测阈值根据管道的制造标准进行应用。一般采用基于ASME B31G(腐蚀管道剩余强度测定手册)的计算结果ERF和安全压力来表示管道状态。有几种方法可以用来描述腐蚀异常的行为,包括穿孔和局部腐蚀。ASME B31G是一个非常保守的标准,可以帮助操作人员避免不必要的切割。它是基于对窄脊船的全尺寸进行一系列广泛试验的经验充分性。基于深度的直方图显示了沿管道长度检测到的所有金属损失特征相对于其位置和表面的分布。针对上述问题的方法可以收集有关管道的基本信息,并对管道的任何异常情况进行总结,具有综合性。
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