Journal
PETROLEUM CHEMISTRY
Volume 61, Issue 1, Pages 67-75Publisher
MAIK NAUKA/INTERPERIODICA/SPRINGER
DOI: 10.1134/S0965544121010072
Keywords
Algerian asphaltenes; adsorption; iron oxide; nanoparticles; Solid-Liquid-Equilibrium
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Funding
- le Ministere de l'enseignement superieur et de la recherche scientifique de l'Algerie
- Dr. Nassar Group for Nanotechnology Research in Chemical and Petroleum Engineering Department, University of Calgary, Alberta, Canada
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This study conducted adsorption experiments on Algerian asphaltene samples extracted from the Hassi Messaoud oil field for the first time using novel synthesized gamma-Fe2O3 nanoparticles. The results demonstrated that the synthesized nanoparticles have high affinity for removing asphaltenes and reached equilibrium after 15 minutes, showing promising potential as nano-adsorbents.
In this study, the adsorption of Algerian asphaltene sample extracted from Hassi Messaoud oil field is conducted for the first time. The adsorption process was performed using novel synthesized iron oxide nanoparticles (gamma-Fe2O3). gamma-Fe2O3 Nanoparticles were in-house synthesized and characterized by an array of techniques using, Brunauer-Emmett-Teller (BET), high-resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD). The results showed that the synthesized nanoparticles have an average crystalline domain size around 10 nm and a specific surface area of 120 m(2)/g. The adsorption process of the Algerian asphaltenes took place in a batch mode by dissolving the asphaltenes in toluene at 25 degrees C. Different initial concentrations of asphaltene solutions were used in this study, namely 100, 500, and 1000 ppm. During this adsorption, both isotherm and kinetic studies were investigated. The results showed that the synthesized iron oxide nanoparticles are promising nano-adsorbents that have a high affinity to remove the asphaltenes and the equilibrium was recorded after 15 min. The Solid-Liquid-Equilibrium (SLE) model was used to correlate the adsorption experimental data.
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