Use of data mining in the corrosion classification of pipelines in catalytic reforming units (CRU)

Abstract

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 is to step in the overall process of knowledge extraction. In this study, one of specific type of decision called the conditional contract arrangement, is used to extract the relevant knowledge in Catalytic Reforming Units (CRU) for 4 factors: Density, PH, total iron ions in vessels (S.FE) and H2S. All of these factors are related to corrosion in CRU 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 0.515 kg/m 3 , PH (water in vessels) is more than 6.7, S.FE is less than 1.5 ppm and H2S in recycle gas is less than 700 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.