Comprehensive Compositional Analysis of Heavy Oil Using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry and a New Data Analysis Protocol

A sequential method that deterministically estimates the molecular composition of heavy oil is reported. A sample of vacuum residua is preseparated into seven fractions: saturated (Sa), aromatic fractions containing one, two, and three or more rings (1A, 2A, and 3A+, respectively), polar and polyaro...

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Bibliographic Details
Published in:Energy & fuels 2021-09, Vol.35 (17), p.13687-13699
Main Authors: Katano, Keita, Teratani, Shogo, Suzuki, Teruo, Tanaka, Ryuzo, Kato, Hiroshi, Norinaga, Koyo
Format: Article
Language:English
Subjects:
Fossil Fuels
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Summary:A sequential method that deterministically estimates the molecular composition of heavy oil is reported. A sample of vacuum residua is preseparated into seven fractions: saturated (Sa), aromatic fractions containing one, two, and three or more rings (1A, 2A, and 3A+, respectively), polar and polyaromatic (Po and PA, respectively) resins, and asphaltene (As). The analysis by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) uses one of three different ionization methods depending on the type of structure. Paraffinic species in Sa are ionized by Ag cationization and laser desorption. Aromatic species in 1A, 2A, 3A+, and Po are ionized by atmospheric pressure photoionization. Poorly volatile and condensed aromatic species in PA and As are ionized by laser desorption ionization. Collision-induced dissociation (CID) is used by the FT-ICR MS instrument to determine the structural building blocks. A new data analysis protocol developed to represent petroleum molecules named the composition and structure analyzer (CSA) requires data obtained before and after CID as inputs. The CSA provides juxtaposed attributes for chemical-structure description (JACDs). Structural information based on JACDs can be used to predict physical and chemical properties such as boiling and melting points and solubility parameters and build attribute-based reaction models, to optimize heavy oil upgrading processes.
ISSN:0887-0624
1520-5029
DOI:10.1021/acs.energyfuels.1c01429