数据挖掘在石脑油加氢装置(NHT)管道腐蚀分类中的应用

A. Samimi, P. Rajeev, A. Bagheri, A. Nazari, J. Sanjayan, Ahmadreza Amosoltani, M. T. Tarkesh Esfahani, S. Zarinabadi
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引用次数: 11

摘要

目前,用于分析和收集石油装置运行数据的计算工具是必不可少的。其中一种方法是在整个知识提取过程中进行分类或回归。在本文中,一种特定类型的决策树算法,称为条件契约安排,是石脑油氢胁迫(NHT)单元的4个因素:密度、pH、容器中总铁离子(S.FE)和H2S。所有这些因素都与NHT装置的腐蚀有关,本文旨在优化一些条件来消除腐蚀。在这方面,使用具有特定范围和pH值的铵水会有所帮助。根据所得结果,最佳密度范围(进料)小于0.712 kg/m3, pH(容器中的水)大于6.5,s - fe小于1.4 ppm, H2S(循环气)小于581 ppm。结果还显示了如何使用此方法来深入了解某些精炼厂,以及如何以可理解和用户友好的方式交付结果。
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Use of data mining in the corrosion classification of pipelines in Naphtha Hydro-Threating Unit (NHT)
Nowadays, computational tools for analyzing and collecting data in the operation of petroleum units are essential. One of the methods is the classification or regression to step in the overall process of knowledge extraction. In this paper, a specific type of decision tree algorithm, called the conditional contract arrangement, is Naphtha hydro-threating (NHT) units for 4 factors: Density, pH, total iron ions in vessels (S.FE) and H2S. All of these factors are related to corrosion in NHT units and this paper aims to optimize some conditions to eliminate it. In this regard, using ammonium water with a specific range and pH can be helpful. According to the obtained results the best range of density (in Feed) is less than 0.712 kg/m3, pH (water in vessels) is more than 6.5, S.FE is less than 1.4 ppm and H2S (in recycle gas) is less than 581 ppm. The outcomes also show how this approach can be used to gain insight into some refineries and how to deliver results in a comprehensible and user-friendly way.
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