Fractionation and Characterization of Asphaltenic and Resinous Fractions of Natural Bitumen
Bibliographic record
Abstract
Communication in Physical Sciences 2020, 5(2) 62-72 Authors: M. T. Bisiriyu, S. Idris, H. G. Aliyu, A. B. Muhammad, A. M. Sokoto & A. M. Abdulkarim Received 14 March 2020/Accepted 08 April 2020 In order to identify the finger prints of some fractions in butimen and to study the changes that take place when heavy oil is upgraded, precipitation, fractionation, and characterization of asphaltenes and resins from natural bitumen were carried out using UV/vis and FTIR Spectroscopy. The sample was purified and the asphaltenes fraction precipitated with n-hexane, fractionated into fractions A and B based on solubility and polarity with hexane/toluene mixture at a fixed ratio while resins recovered from maltenes was fractionated into subfractions X, Y, Z by liquid adsorption chromatography on a silica/alumina adsorption column with dichloromethane/methanol mixed solvent in the ratio of 4:1, 3:2 and :2:3, respectively. The results showed that crude asphaltene and its subfractions absorbed light of longer wavelength (465 – 640 nm), indicating that they are made up of polynuclear aromatic compounds. The results also revealed the presence of alkyl side chains and major oxygenated groups in both crude asphaltene and its subfractions (A and B). The spectra of crude resin and its subfractions showed that they absorbed radiation of longer wavelength (490-580 nm), which are character of polynuclear aromatic compounds with the extended conjugated system. However, IR spectra revealed that all the fractions including the crude resin contained aromatic rings with alkyl side chains and oxygenated group. References Aemani, M. & Rabbani, A.R. (2015). Oil-oil Correlation by FTIR specroscopy of Asphaltenes Samples. Geoscience Journal, doi: 10:1007/s12303-015-0042-1. Anigbogu, I. V. (2011). Precipitation of Asphaltenes, Quantification of Maltenes, UV and FTIR Spectroscopic Studies of C7 and C5 + C7 Asphaltenes from 350 oC Atmospheric Residuum Crudes. MSc. Dissertation, University of Nigeria, Nsukka. P. 130. Bakare, H.O., Esan, A.O. & Olabemiwo, O.M. (2015). Characterization of Agbabu Natural Bitumen and Its Fractions Using Fourier Transformed Infrared Spectrometry. Chemistry and Materials Research, 7, pp. 1-11. Barrera, D., Ortiz, D. & Yarranton, H. (2013). Molecular Weight and Density Distributions of Asphaltenes from Crude Oils. Energy and Fuels, 27, pp. 2474- 2487. Bisiriyu, M. T., Idris, S., Garba, H. A., Yelwa, A. S., Muhammad, A. B. & Faruq, U. Z. (2017). Characterization of Virgin Asphaltenes and itsSubfractions using UV-Visible and FTIRSpectroscopy. Physical Sciences First Biennial International Conference Proceedings 4-5th May, 2017, FUT, Minna, pp. 35-45. Coelho, R.R., Hovell, I., Lopez-Moreno, E., de Souza, A.L. & Rajagopal, K. (2011). Characterization of Functional Groups of Asphaltenes in Vacuum Residue using Molecular Modelling and FTIR Techniques. Petroleum Science and Technology, 25, 1-2, pp. 34-41 Concawe’s PP/Analytical Information Task Force (2012). Reach-Analytical Characterisation of Petroleum UVCM Substances.www .concawe. org . p 118 Cruz, E .E. B., Rivas, N. V. G., Garcia, U. P., Martinez, M. M. & Banda, J.M.M. (2017). Characterization of Crude Oil and the Precipitated asphaltenes fraction using UV spectroscopy, Dynamic Light Scattering and Microscopy. Insight in Petroleum Science and Technology, doi: 10:5772/inteopen.70108. Gafonova, O.V. (2000). Role of Asphaltenes and Resin in the Stabilzation of Water-inhydrocarbon Emulsions. M.Sc. Thesis, University of Calgary. Grinko, A. A., & Golovko, A. K. (2011). Fractionation of Resins and Asphaltenes and investigation of their Composition and Structure Using Heavy Crude from USA Field as an Example, Petroleum Chemistry, 51; pp. 192202. Hashem-Kiasari, H., Hemmati-Sarapardeh, A., Mighani, S., Mohammadi, A. H.,& SadaeeSola, B. (2014). Effect of Operation Parameters on SAGD Performance in a Dip Heterogeneous Fracture Researvoir, Fuel, 122, pp. 82-93. Li, M., Xu, M., Ma, Y., Wu, Z. & Christy, A. A. (2002). The Effect of Molecular Parameters on the Stability of Water-in-Crude Oil Emulsions Studied by IR and UV Spectroscopy: A Physicochemical and Engineering Aspect. Colloids and Surfaces, 197, pp. 193-201. Mat, H.B., Samsuri, A., Abdulrahman, W.A.W & Rani, S.I. (2006). Study on Demulsifier Formation for Treating Malaysian Crude Oil Emulsion. University Technology Malaysia, 158pp. Muhammad, A. B. (2015). Thermal Evolution of Aliphatic and Aromatic Moieties of Asphaltenes from Coals of Different Rank: Possible Implication to the Molecular Architecture of Asphaltenes. China Journal of Geochemistry. doi:10.1007/s11631-015-0041-y. 34, 3, pp. 422–430. Muhammad, A. B. (2009). The Molecular Composition and Geocheical Applications of Asphaltenes. Ph.D Thesis, Newcastle University. UK. P 199. Muraza, O. & Galadima, A. (2015). Aquathermolysis of Heavy Oil: A Review and Perspective on Catalyst Development. Fuel, 157, pp. 219-231. Poveda-Jaramillo, J.C. Molina-Velasco, D.R., Bohorques-Toleda, N.A., Torres, M.H. & ArizaLeon, E. (2016). Chemical Characterization of the Asphaltenes from Colombian Colorado Light Crude Oil. CT & F –Ciencia, Tecnologia, Futuro, 6, 3, pp. 105-1. Wilt, B.K. Elch, W.T. & Ramkin, J.G. (1998). Determination of Asphaltenes in Petroleum Crude by Fourier Transformed Infrared Spectroscopy. Energy Fuels, 12, 5, pp. 10081012. Yamchi, H. S. (2014). Effect of Refining on Asphaltene Property Distributions. M.Sc. Thesis, University of Calgary, Alberta, 218p
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".