Application of regression models on the prediction of corrosion degradation of a crude oil distillation unit

• Autorzy: Varbai Balázs , Wéber Richárd , Farkas Balázs , Danyi Péter , Krójer Antal , Locskai Roland , Bohács György , Hős Csaba

Identyfikatory

DOI

10.2478/adms-2024-0005

• Języki publikacji: EN

Abstrakty

EN

The crude distillation unit is the most critical elements in the refining process. Moreover, most of the equipment in the distillation unit are made of general carbon steels. Data analysis models, machine learning techniques can predict corrosion degradation rates. We used Pearson’s correlation coefficient and multiple linear regression, to predict the impact of process parameters. Altogether, we have analysed 84 channels of technological parameters, and 22 different types of crude oils. Among the corrosion agents, the chloride content strongly affected the weight loss of coupons, where the highest coefficient was 0.68. The most influential parameter is found to be the pH value. Thus, an estimation method of the pH value is set up to predict the corrosion degradation rate. The regression correlation for estimating the pH value is 0.53 if the corrosion agents are not used, which can be improved to 0.76 if the corrosion agents are also used in the regression analysis.

Słowa kluczowe

EN

regression analysis; corrosion prediction; predictive maintenance; carbon steel

PL

analiza regresji; korozja; utrzymanie predykcyjne; stal węglowa

• Wydawca: Politechnika Gdańska
• Czasopismo: Advances in Materials Science
• Rocznik: 2024
• Tom: Vol. 24, nr 1(79)
• Strony: 72--85
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Varbai Balázs

• Department of Materials Science & Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary

autor

Wéber Richárd

• Department of Hydrodynamic Systems, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary

autor

Farkas Balázs

• MOL Plc., Hungary, Hungary

autor

Danyi Péter

• MOL Plc., Hungary, Hungary

autor

Krójer Antal

• RozsdaLovag Ltd., Hungary

autor

Locskai Roland

• RozsdaLovag Ltd., Hungary

autor

Bohács György

• Green Italy Srl., Cagliari, Italy

autor

Hős Csaba

• Department of Hydrodynamic Systems, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).