Mass fraction

Algorithm

Residual

Residual oil

Engineering

Distillation

Materials science

Mole fraction

Petroleum engineering

Computer science

Asphaltene

Fraction (chemistry)

Geology

Physical chemistry

Biochemical engineering

Vacuum distillation

Nanotechnology

Organic chemistry

Characterization (materials science)

Environmental science

Analytical Chemistry (journal)

Chemistry

Osmometer

Chemical composition

Journal of Petroleum Science and Engineering

This paper passes over the literature for residual oil characterization. The methods for residual oil characterization are classified in: characterization based on physical and chemical bulk properties; characterization based on saturates, aromatics, resins, asphaltenes (SARA) analysis; and characterization based on the molecular modeling processes. The upper and lower limits of the values for physical and chemical bulk properties of the vacuum residual oils around the world are outlined. A variation of the physical and chemical bulk properties of SARA fractions originating from different vacuum residual oils is discussed. Correlations between refractive index and density, and between the vapor pressure osmometry (VPO) molecular weight of saturates and aromatics were found. A ratio between the lowest and the highest molecular weight of the vacuum residual oil saturate and aromatic fraction of 2.4 was found showing that the molecular weights of these SARA fractions are not fixed and can vary between 360 and 880 g/mole for the saturates, and 450 and 1080 g/mole for the aromatics. Assuming the same ratio in variation of molecular weights (MW) of the fractions resins and asphaltenes for the reviewed 41 vacuum residual oils suggests that the resins MW could vary in the range 800 ÷ 1800 g/mole and the asphaltenes MW may vary between 1000 and 3000 g/mole. The application of sophisticated analytical techniques showed a very big diversity in the species building the vacuum residual oils and different distribution of these species even for the same SARA fraction depending on the residual oil origin. The different approaches for “molecular reconstruction” of residual oils Monte Carlo (MC), Quadrature algorithm (QA), Entropy maximization (EM), quantitative structure-property relationship (QSPR), structure-oriented lumping (SOL), molecular type homologous series (MTHS) matrix, artificial neural network (ANN) and multiple linear regression (MLR) are reviewed. A conclusion is made that the advanced analytical and computational techniques are still an obligor to the chemical engineers working at the commercial residual oil conversion units who aim to optimize the conversion process performance.

Challenges in characterization of residual oils. A review