Cluster analysis of crude oils with k-means based on their physicochemical properties. (January 2022)

• Record Type: Journal Article
• Authors: Sancho, A.; Ribeiro, J.C.; Reis, M.S.; Martins, F.G.
• Is Part Of: Computers & chemical engineering. Volume 157(2022)
• Publication Date: 2022-01
• Subjects: Crude oil -- Cluster analysis -- k-means -- Internal validation -- Unsupervised learning; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107633
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Physicochemical properties of crude oils have a structure suitable for clustering. k-means with 3 clusters provides additional knowledge to better group crude oils. Usage of internal validation indexes to measure clustering quality is relevant. Abstract: The values of the physicochemical properties of crude oils vary significantly, depending on their geographical origins. A standard categorization of crude oils is grossly based on the density and sulfur content, not considering other properties that can have meaningful impacts on blending and in some refining processes. Cluster analysis is an unsupervised machine learning technique that categorizes observations based on their similarity. In this work, k-means clustering algorithm was applied to a wide range of physicochemical properties to identify groups of crudes oils with high affinity that possibly have similar behavior later on, in downstream operations. A data set from Galp SA refineries (located in Portugal) containing 454 observations, corresponding to values of 9 properties, from 45 different crude oil sources was used in the present analysis. After suitable preprocessing, k-means was applied using different cluster numbers, and their performance was evaluated through the internal validation metrics silhouette index and Local Cores-based Cluster Validity (LCCV) index. The recommend number of clusters was 3, which presented the best performance with a LCCV index of 0.39. Crude oils from the same source should be incorporated in the same cluster, and this was corroborated by external validation, with 1.8% of the observations were placed in a different cluster than the majority of same source crude oils. The proposed method was also able to identify observations with unusually high iron contents concerning the same source of crude oils when more clusters were considered. This work provides a methodology to obtain a better categorization of crude oils by using cluster analysis, allowing the refineries to know how similar crude oils and their sources are. This categorization is very useful for improving the formulation of crude blends and the crude oils quality control, with the goal to optimize further the refining operations.