Loading...

Development of Chemometric Pattern Recognition Methods Combined with Gas Chromatography for The Analysis of Chromatographic Fingerprints of Crude Oils and Identification of Thier Sources

Hashemi Nasab, Fatemeh | 2018

521 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51387 (03)
  4. University: Sharif University of Technology
  5. Department: Chemistry
  6. Advisor(s): Parastar Shahri, Hadi
  7. Abstract:
  8. In recent years, with increasing spills of oil and related petroleum products in the marine environment , the need for source identification of crude oil is increasing rapidly. Oil spill can have serious biological and economic impacts. There are many oil tankers on the surface of the sea and 0.68х109 kg crude oil spilled into soil per year . The source identification of oil spills can be a considerable challenge so these studies analytically and environmentally are important. The purpose of this study was to provide an update of the state-of-the-art of oil fingerprinting techniques to demonstrate the use of a rapid, inexpensive and useful technique for distinguishing between crude oils. In this regard, a fractionation method based on SARA test was used to divide nine crude oils (obtained from Sharif upstream petroleum institute) into aliphatic (saturate), aromatic, resin and asphaltene. It is necessary to remove the asphaltene fraction before proceeding with chromatography. After fractionation, three fractions of nine oil samples were analyzed by GC-FID and GC-MS. 9 crude oils were analyzed by FT-IR.The obtained fingerprints were baseline corrected, aligned and auto-scaled and then analyzed using unsupervised classification methods of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Evaluation of PCA score plot (explaining 93.69% of variance accounted for three PCs) showed that aromatic fractions belong to three classes and result of HCA with Ward’s method confirmed that. The clustering results of aliphatic and resin fractions also showed the presence of 3 classes but due to their different composition, classes were not the same. The results of unsupervised classification were then used a starting point for supervised classification methods of Partial Least Square Discriminant Analysis (PLS-DA) and Counter propagation Artificial Neural network (CPANN). The results of PLS-DA analysis for aromatics showed best discrimination compared to other fractions.According to all the analysis, aromatic fractions were appropriate for identification of crude oils sources and this is due to its wide range of compounds. IR analysis were appropriate for these purpose as a simple, inexpensive and available method. These results have helped to propose a methodology for the clustering and classification of crude oils
  9. Keywords:
  10. Pattern Recognition ; Crude Oil ; Gas Chromatography ; Chemometrics Method ; Chromatographic Fingerprint ; Supervised Analysis ; Unsupervised Analysis

 Digital Object List

 Bookmark

No TOC