Thermodynamic Scaling of the Shear Viscosity of Lennard-Jones Chains of Variable Rigidity

In this work, the thermodynamic scaling framework has been used to emphasize the limitations of fully flexible coarse grained molecular models to yield shear viscosity of real liquids. In particular, extensive molecular dynamics simulations have confirmed that, while being reasonable to describe the viscosity of short normal alkanes, the fully flexible Lennard-Jones and Mie chains force fields are inadequate to capture the density dependence of shear viscosity of medium to long alkanes. In addition, it is shown that such a weakness in terms of coarse grained molecular models can be readily quantified by using the thermodynamic scaling framework. As a simple alternative to these force fields, it is demonstrated that the insertion of a variable intramolecular rigidity in the Lennard-Jones chains model exhibits promising results to model medium to long chain-like real fluids from both thermodynamic and viscosity points of view.

Modeling of nitrogen solubility in normal alkanes using machine learning methods compared with cubic and PC-SAFT equations of state

Accurate prediction of the solubility of gases in hydrocarbons is a crucial factor in designing enhanced oil recovery (EOR) operations by gas injection as well as separation, and chemical reaction processes in a petroleum refinery. In this work, nitrogen (N2) solubility in normal alkanes as the major constituents of crude oil was modeled using five representative machine learning (ML) models namely gradient boosting with categorical features support (CatBoost), random forest, light gradient boosting machine (LightGBM), k-nearest neighbors (k-NN), and extreme gradient boosting (XGBoost). A large solubility databank containing 1982 data points was utilized to establish the models for predicting N2 solubility in normal alkanes as a function of pressure, temperature, and molecular weight of normal alkanes over broad ranges of operating pressure (0.0212–69.12 MPa) and temperature (91–703 K). The molecular weight range of normal alkanes was from 16 to 507 g/mol. Also, five equations of state (EOSs) including Redlich–Kwong (RK), Soave–Redlich–Kwong (SRK), Zudkevitch–Joffe (ZJ), Peng–Robinson (PR), and perturbed-chain statistical associating fluid theory (PC-SAFT) were used comparatively with the ML models to estimate N2 solubility in normal alkanes. Results revealed that the CatBoost model is the most precise model in this work with a root mean square error of 0.0147 and coefficient of determination of 0.9943. ZJ EOS also provided the best estimates for the N2 solubility in normal alkanes among the EOSs. Lastly, the results of relevancy factor analysis indicated that pressure has the greatest influence on N2 solubility in normal alkanes and the N2 solubility increases with increasing the molecular weight of normal alkanes.

Evaluation of crude oil biodegradation using mixed fungal cultures

The use of potent fungal mixed cultures is a promising technique for the biodegradation of crude oil. Four isolates of fungi, namely, Alternaria alternata (AA-1), Aspergillus flavus (AF-3), Aspergillus terreus (AT-7), and Trichoderma harzianum (TH-5), were isolated from date palm soil in Saudi Arabia. The mixed fungal of the four isolates have a powerful tool for biodegradation up to 73.6% of crude oil (1%, w/v) in 14 days. The fungal consortium no. 15 containing the four isolates (1:1:1:1) performed significantly better as a biodegradation agent than other consortium in a variety of environmental factors containing crude oil concentration, incubation temperature, initial pH, biodegradation time and the salinity of the medium. The fungal consortium showed better performance in the biodegradation of normal alkanes (n-alkanes) than that of the polycyclic aromatic hydrocarbons (PAHs); the biodegradation efficiency of normal alkanes of the fungal consortium (67.1%) was clearly high than that of the PAHs (56.8%).

The use of biological markers in organic geochemical investigations of the origin and geological history of crude oils (I) and in the assessment of oil pollution of rivers and river sediments of serbia (II)

Biological markers (BMs) are organic compounds in oils in which a precursor is known, during the transformation of organic matter these compounds undergo certain structural and stereochemical changes. Based on the established precursors of BMs, the origin of the examined oils can be estimated, and based on the intensity and type of changes, geological history. It includes defining the deposition medium, the degree of maturation, the length of the oil migration path, the degree of biodegradation. The most studied and applied BMs are normal alkanes, isoprenoid aliphatic alkanes pristane and phytane, and polycyclic alkanes of the sterane and terapane type. On the other hand, in environmental chemistry, these compounds can significantly contribute to the identification of petroleum pollutants, as well as to the assessment of the migration mechanism and the intensity of biodegradation. This review paper first presents the results related to the application of BMs in organic geochemical correlations of oil in the southeastern part of the Pannonian Basin (I). The second part provides an overview of those researches in which the same BMs were used in the identification of oil pollutants and in monitoring its changes during migration and biodegradation in rivers and river sediments of Serbia (II).