Estimation of gas solubility in petroleum fractions using PR-UMR and group contributions methods

Aguilar-Cisneros, Humberto; Uribe-Vargas, Verónica; Carreón‐Calderón, Bernardo

doi:10.1016/j.fuel.2020.117911

Cited by 5 publications

(2 citation statements)

References 80 publications

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“…Near-boiling or azeotropic systems are often separated by extractive distillation, and the choice of solvent determines the separation efficiency. − In extractive distillation, the selection of the best solvent can not only improve the purity of the product but also reduce the energy consumption of the system and save cost. At present, many methods can be used in solvent screening, the empirical method, experimental determination method, , group contribution method, , COSMO-RS model method, , etc. The results of the experimental measurement are the most reliable.…”

Section: Introductionmentioning

confidence: 99%

Isobaric Vapor–Liquid Equilibrium of Binary Systems of 1-Pentanol + Butyl Butyrate, 1-Pentanol + N-Formylmorpholine, and p-Xylene + Butyl Butyrate at 101.3 kPa

et al. 2021

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In this work, the effects of N-methyl pyrrolidone, N,N-dimethylformamide, cyclohexanol, butyl butyrate, and N-formylmorpholine on the separation efficiency of a p-xylene and 1-pentanol azeotropic system are investigated, and the best solvent is selected from these solvents. The vapor–liquid equilibrium data of binary systems of 1-pentanol + butyl butyrate, 1-pentanol + N-formylmorpholine, and p-xylene + butyl butyrate are measured using a modified Rose-type circulating still under 101.3 kPa. In order to check the thermodynamic consistency of the experimental vapor–liquid equilibrium data, the Herington and Van Ness methods are used. In addition, the nonrandom two-liquid, universal quasichemical, and Wilson models are used to correlate the experimental data of binary systems, and the interaction parameters of binary systems are obtained.

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Section: Introductionmentioning

confidence: 99%

Isobaric Vapor–Liquid Equilibrium of Binary Systems of 1-Pentanol + Butyl Butyrate, 1-Pentanol + N-Formylmorpholine, and p-Xylene + Butyl Butyrate at 101.3 kPa

et al. 2021

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“…Ultimately, PR-EOS was implemented to predict gas solubilities 45 . Aguilar-Cisneros et al 46 determined the solubility of N 2 , CO 2 , and CH 4 in petroleum fluids using the PR-EOS in conjunction with various mixing rules in systems including bitumens, heavy oils, refinery cuts, and coal liquids. The universal and van der Waals mixing rules revealed satisfactory outcome between experimental data and predicted values, while the modified Huron-Vidal of order one mixing rule produced large discrepancies 46 .…”

Section: Introductionmentioning

confidence: 99%

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

Madani

Mohammadi

Atashrouz

et al. 2021

Sci Rep

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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.

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Phase Equilibrium

Carreón‐Calderón¹,

Uribe-Vargas²,

Aguayo³

2020

Thermophysical Properties of Heavy Petroleum Fluids

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Estimation of gas solubility in petroleum fractions using PR-UMR and group contributions methods

Cited by 5 publications

References 80 publications

Isobaric Vapor–Liquid Equilibrium of Binary Systems of 1-Pentanol + Butyl Butyrate, 1-Pentanol + N-Formylmorpholine, and p-Xylene + Butyl Butyrate at 101.3 kPa

Isobaric Vapor–Liquid Equilibrium of Binary Systems of 1-Pentanol + Butyl Butyrate, 1-Pentanol + N-Formylmorpholine, and p-Xylene + Butyl Butyrate at 101.3 kPa

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

Phase Equilibrium

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