Heavy Petroleum Supercritical Fluid Deasphalting Process Simulation Based On the Saturate, Aromatic, Resin, and Asphaltene Composition

In the presented work, we developed a phase equilibrium model for the solvent deasphalting process of heavy petroleum. The pseudocomponents of the heavy oil feedstock were defined according to the saturate, aromatic, resin, and asphaltene composition. A set of empirical correlations was proposed using the molecular weight, the hydrogen carbon ratio, and the aromatic carbon ratio to predict the thermodynamic properties of the pseudocomponent. The vapor–liquid flash calculation and the cubic equation of state were used to calculate the compositions and properties of different phases at various conditions. The effects of the feedstock, the temperature, the pressure, and the solvent ratio on the key product properties were investigated. The model prediction agrees with the experimental data and is expected to guide the design and condition optimization of the solvent deasphalting process

Quantitative Structure–Property Relationship Model for Hydrocarbon Liquid Viscosity Prediction

The liquid viscosity of hydrocarbon compounds is essential in the chemical engineering process design and optimization. In this paper, we developed a quantitative structure–property relationship (QSPR) model to predict the hydrocarbon viscosity at different temperatures from the chemical structure. We collected viscosity data at different temperatures of 261 hydrocarbon compounds (C₃–C₆₄), covering <i>n</i>-paraffins, isoparaffins, olefins, alkynes, monocyclic and polycyclic cycloalkanes, and aromatics. We regressed the experimental data using an improved Andrade equation at first. Hydrocarbon viscosity versus temperature curves were characterized by only two parameters (named <i>B</i> and <i>T</i>₀). The QSPR model was then built to capture the complex dependence of the Andrade equation parameters upon the chemical structures. A total of 36 key chemical features (including 15 basic groups, 20 united groups, and molecular weights) were manually selected through the trial-and-error process. An artificial neural network was trained to correlate the Andrade model parameters to the selected chemical features. The average relative errors for <i>B</i> and <i>T</i>₀ predictions are 2.87 and 1.05%, respectively. The viscosity versus temperature profile was calculated from the predicted Andrade model parameters, reaching the mean absolute error at a value of 0.10 mPa s. We also proved that the established QSPR model can describe the viscosity versus temperature profile of different isomers, such as isoparaffins, with different branch degrees and aromatic hydrocarbons with different substituent positions. At last, we applied the QSPR model to predict gasoline and diesel viscosities based on the measured molecular composition. A good agreement was observed between predicted and experimental data (absolute mean deviation equals 0.21 mPa s), demonstrating that it has capacity to calculate viscosity of hydrocarbon mixtures

Recovering Valuable Hydrocarbon Molecules from Used Lubricating Oils via Supercritical CO₂ Extraction

With the escalating global energy crisis and burgeoning environmental concerns as a result of the accumulation of used lubricating oil, the urgency and economic potential of recycling this waste is undeniably critical. Conventional methods, such as distillation and hydrogenation, often stumble upon operational difficulties as a result of thermochemical decomposition, while advanced procedures, like molecular distillation and membrane separation, face industrial scale-up, owing to prohibitive costs and limited separation capabilities. Herein, this study heralds the emergence of supercritical CO2 (SC-CO2) extraction, which can exemplify superior efficacy in addressing these concerns. Leveraging the high solvency under extreme pressure and low-temperature conditions, SC-CO2 can selectively separate undesirable components, reducing coke formation and simplifying subsequent refining stages. The comprehensive analysis, conducted using gas chromatography–mass spectrometry and high-resolution mass spectrometry, revealed the selectivity of SC-CO2 toward saturates with a smaller molecular weight. On the basis of this analysis, the optimal extraction condition was determined. Implementing a two-stage process at 70 °C and 10–20 MPa, we effectively eradicated residual small molecules and large polar compounds, yielding an intermediate fraction rich in isoparaffins and 1–4 ring naphthenes with a carbon distribution of C25–40. Notably, the recovery rate of saturates was 60%. Critically, the removal rate of undesirable components in recycled lubricating oil is remarkably high, reinforcing the practical viability and exceptional advantages of SC-CO2 extraction for lubricating oil recycling as a sustainable and economically rewarding solution to pressing global energy and environmental predicaments

Molecular Characterization of Vacuum Resid and Its Fractions by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Various Ionization Techniques

Venezuela Orinoco extra-heavy-crude-oil-derived vacuum resid (VR) was subjected to supercritical fluid extraction and fractionation (SFEF) to prepare multiple narrow fractions. The SFEF fractions were analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with various ionization techniques, including positive-ion electrospray ionization (ESI), negative-ion ESI, positive-ion atmospheric pressure photoionization (APPI), and sulfur methylation followed by positive-ion ESI. The results showed that the SFEF separates the VR species by their molecular weights and degrees of molecular condensation. The mass ranges of compounds determined by various ionization techniques were comparable. The FT-ICR MS data were in agreement with the elemental analysis and molecular weight determined by gel permeation chromatography (GPC) and vapor pressure osmometry (VPO) for the extractable fractions. The molecular compositions of SFEF fractions determined by FT-ICR MS provide important clues for the understanding of the molecular composition for the unextractable end-cut (asphaltenes). Each ionization technique favors identification of certain compounds in heavy petroleum fractions and discriminates against others. APPI allows for a general overview of species present in heavy petroleum fractions, because of its ability to ionize a wide range of species. ESI is more selective toward polar species. A thorough characterization of species in heavy petroleum fractions cannot be achieved by using an ionization technique; however, it can be performed by combining various ionization techniques

Hindered Stepwise Aggregation Model for Molecular Weight Determination of Heavy Petroleum Fractions by Vapor Pressure Osmometry (VPO)

Venezuela Orinoco heavy crude oil was fractionated into diesel, vacuum gas oil (VGO), vacuum residue (VR), and asphaltene fractions, which were subjected to molecular weight (MW) measurement by vapor pressure osmometry (VPO). The VPO is known to overestimate the average molecular weight (MW) of heavy hydrocarbons, because of molecular aggregation. This paper proposes a hindered stepwise aggregation (HSA) model to simulate the molecular aggregation and used the model to estimate the true MW of heavy petroleum fractions. A data regression procedure was developed to determine the model parameters, aggregation equilibrium constant, and aggregate distribution, using a fast simulated annealing (FSA) algorithm based on the VPO data. This data analysis method is self-tuned to fit the VPO data to the HSA models of various petroleum fractions using the optimized solution of the FSA algorithm. The results showed that the VPO data of heavy petroleum fractions at various solution concentrations were in good agreement with those predicted by the HSA model. The aggregation equilibrium constant and aggregate distribution data obtained from the HSA model suggested that various degrees of molecular aggregation occur in heavy petroleum fractions. The molecules of diesel and VGO were monomers, regardless of the solution concentration. The molecules of VR formed dimer aggregates at high solution concentrations; the number of dimer aggregates exceeded that of monomers as the solution concentration increased. The molecules of asphaltenes were polymer aggregates. The size of asphaltene polymer aggregates increased significantly with the solution concentration. The MW of asphaltenes determined by the HSA model was much lower than that by the conventional linear regression method

Molecular Representation of Petroleum Vacuum Resid

A novel methodology was extended for modeling the detailed composition of petroleum heavy vacuum resid fractions. The resid molecules were organized in terms of basic structural attributes: cores, intercore linkages, and side chains. The identities of the structural attributes were determined both from the extrapolation of chemical characteristics of light petroleum and the analysis of detailed mass spectrometric measurement of heavy resid fragmentation products. A building block library was constructed containing ∼600 attributes. The molecular composition was constructed by the combination of attributes, or building blocks, into discrete molecules. The quantitative abundance of each molecule was determined by the juxtaposition of a set of structural attribute probability density functions (PDFs) constraining pure hydrocarbon and heteroatom mixtures. Quantitative structure–property relationships (QSPRs) were applied to calculate the bulk properties of both the constructed molecules and the mixture. The adjustable parameters of the PDFs were determined using an optimization loop that employed an objective function that contained a term for each of the available analytical data points. The resulting optimal molecular compositions were in good agreement with the experimental structural information

Molecular-Level Kinetic Modeling of Resid Pyrolysis

A molecular-level kinetic model of heavy oil pyrolysis was developed for a Venezuelan vacuum residue. Model development proceeded in three major steps: creation of a molecular description of the feedstock, generation of a reaction network, and model solution and parameter tuning. The feedstock composition, as described in previous work [Zhang et al. <i>Energy Fuels</i> <b>2014</b>, <i>28</i>, 1736–1749], was modeled in terms of probability density functions (PDFs) of three finite attribute groups (385 cores, 2 intercore linkages, and 194 side chains) and a PDF for each of a cluster-size and binding site distribution. These attributes, or molecule building blocks, represent more than 0.4 M molecules. An attribute reaction network was developed using the fundamental reaction chemistry for resid pyrolysis including 6274 reactions that fall into one of 11 reaction families. To make solution time tractable, we used attribute reaction modeling (ARM) which constrained the number of material balances to the number of attributes and irreducible molecules in the system or 2841 total equations. Therefore, reactor output was a set of reaction-altered attribute PDFs and molar amounts of irreducible molecules. The quantitative molecular composition of the reactor outlet was obtained through the juxtaposition of the final attribute PDFs. The properties of both the sampled molecules and the char fraction were obtained using quantitative structure–property relationships (QSPRs). The kinetic model was tuned using a least-squares objective function comparing the model predictions to measurements from the molecular to bulk-property level for all relevant boiling point fractions. The tuned model showed reasonably good agreement with the experimental measurements

Molecular Characterization of Polar Heteroatom Species in Venezuela Orinoco Petroleum Vacuum Residue and Its Supercritical Fluid Extraction Subfractions

A Venezuela Orinoco petroleum vacuum residue (VR) was subjected to supercritical fluid extraction fractionation (SFEF) and separated into 13 extractable fractions and an unextractable end-cut. Detailed molecular composition of polar heteroatom species in the SFEF subfractions were determined by electrospray ionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The SFEF subfractions were also subjected to high-temperature gas chromatography (GC) for their simulated distillation analysis, gel permeation chromatography (GPC) for their molecular distributions, and open column liquid chromatography for their saturates, aromatics, resins, and asphaltenes (SARA) compositions. In ESI FT-ICR analysis, the mass spectra showed that the mass range and maximum peak of the SFEF subfraction increased as the SFEF subfraction became heavier. Multifunctional group compounds, such as N₁S₁, N₁S₂, N₁O₁, and N₂, show high relative abundance in heavier subfractions. The double bond equivalence (DBE) values and carbon numbers of all class species increased steadily as the SFEF subfraction became heavier. This indicated that the molecules in various SFEF subfractions are separated by their aromaticity and molecular weight. The SFEF end-cut could not be thoroughly characterized by ESI because of its low intensity, while basic species detected by positive-ion ESI were suppressed by a strong response of metal porphyrin species. Results from GPC and SARA compositional analysis show that the end-cut enriches most of the asphaltene in feedstock and has the highest apparent molecular size