Multivariate data analysis applied in the evaluation of crude oil blends
| Autor: | Mayara Cristina Pinto da Silva, Valdemar Lacerda, Rayane R.B. Corona, Francine D. dos Santos, Cristina M. S. Sad, Eustáquio V.R. Castro, Natália A. Portela, Laine B. Pereira, Samantha R.C. Silva, Paulo R. Filgueiras |
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| Rok vydání: | 2019 |
| Předmět: |
Multivariate statistics
Multivariate analysis business.industry 020209 energy General Chemical Engineering Organic Chemistry technology industry and agriculture Energy Engineering and Power Technology 02 engineering and technology Crude oil Hierarchical clustering Fuel Technology 020401 chemical engineering Outlier Principal component analysis 0202 electrical engineering electronic engineering information engineering Environmental science 0204 chemical engineering Process engineering business |
| Zdroj: | Fuel. 239:421-428 |
| ISSN: | 0016-2361 |
| DOI: | 10.1016/j.fuel.2018.11.045 |
| Popis: | In this paper, monitoring of the physicochemical properties of crude oil blends during production stages is described. The data of the properties of crude oil blends were obtained by laboratory characterization, and then analyzed by principal component analysis (PCA), hierarchical cluster analysis (HCA), and Mahanalobis distance. Thus, the quality of the blends was monitored quickly with simple multivariate tools. The results indicate that a change in the contribution of different wells in the blends caused a change in the profile. The PCA demonstrated that in each period, the physicochemical properties in the blends contributed to verifying the spread of the data. The blends could be organized by HCA, and it was possible to identify outlier samples with different quality standards for the oil. This information is important because it allows checking the changes in the oil profile, which helps in making adjustments to improve the quality of the final product in the primary process. |
| Databáze: | OpenAIRE |
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