Experimental study and modelling of asphaltene deposition on metal surfaces with superhydrophobic and low sliding angle inner coatings

Inner coatings have emerged as a novel technique to prevent the deposition of paraffin, wax, scale, and corrosion of pipelines during oil production and transport. Few studies addressed this technique for preventing asphaltene deposition. In this study, two superhydrophobic inner coatings, including...

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Veröffentlicht in:Scientific reports 2021-08, Vol.11 (1), p.16812-16812, Article 16812
Hauptverfasser: Haji-Savameri, Mohammad, Norouzi-Apourvari, Saeid, Irannejad, Ahmad, Hemmati-Sarapardeh, Abdolhossein, Schaffie, Mahin, Mosavi, Amir
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Sprache:eng
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Zusammenfassung:Inner coatings have emerged as a novel technique to prevent the deposition of paraffin, wax, scale, and corrosion of pipelines during oil production and transport. Few studies addressed this technique for preventing asphaltene deposition. In this study, two superhydrophobic inner coatings, including polytetrafluoroethylene (PTFE) coating and nanosilica coating, were fabricated on metal surfaces and the asphaltene deposition on these coated surfaces was examined. A model oil solution was prepared using asphaltene and heptol and the effect of static and dynamic flow states on the amount of asphaltene deposition on uncoated electrodes, PTFE coated electrodes, and nanosilica coated electrodes were investigated. The results showed that the PTFE coating is more effective in reducing asphaltene deposition than nanosilica coating. The PTFE coating could reduce 56% of the deposition in a static state and more than 70% in a dynamic state at an asphaltene concentration of 2000 ppm. For PTFE coating in a dynamic state, the deposition rate is negligible in long times. In addition, it was found that the type of flow state affects the asphaltene deposition kinetics. The results demonstrate that, in the static state, the nth-order kinetics model, and in the dynamic state, the double exponential models are in best agreement with the experimental data.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-95657-5