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

Madani, Seyed Ali; Mohammadi, Mohammad-Reza; Atashrouz, Saeid; Abedi, Ali; Hemmati-Sarapardeh, Abdolhossein; Mohaddespour, Ahmad

doi:10.1038/s41598-021-03643-8

Cited by 17 publications

(4 citation statements)

References 100 publications

(81 reference statements)

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“…Modeling using PC-SAFT for the binary system n -hexadecane/N 2 has been also carried out by Madani et al . using a machine-learning algorithm, as well as by Rowane et al and García-Sánchez et al However, Madani et al do not report any k ij PCS in tabular form, while the k ij PCS of Rowane et al cannot be directly compared to our fitted k ij PCS , since the pure compound parameters m , σ, and ε were obtained by a group contribution method. García-Sánchez et al considered only the VLE data of Lin et al for their fit and report with k ij PCS = 0.1816 and k ij PCS = 0.1860, two slightly different values depending on the objective function used.…”

Section: Resultsmentioning

confidence: 96%

“…The latter are furthermore visualized together with the PPR78-predicted binary interaction coefficients k ij, Td PR in the Appendix section. Modeling using PC-SAFT for the binary system nhexadecane/N 2 has been also carried out by Madani et al 37 using a machine-learning algorithm, as well as by Rowane et al 2 and Garci ́a-Sańchez et al 38 However, Madani et al 37 do not report any k ij PCS in tabular form, while the k ij PCS of Rowane et al 2 cannot be directly compared to our fitted k ij PCS , since the pure compound parameters m, σ, and ε were obtained by a group contribution method. Garci ́a-Sańchez et al 38 considered only the VLE data of Lin et al 9 for their fit and report with k ij PCS = 0.1816 and k ij PCS = 0.1860, two slightly different values depending on the objective function used.…”

Section: Vle Datamentioning

confidence: 99%

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Vapor–Liquid Equilibria of Binary Systems Consisting of Nitrogen and n-Tridecane, n-Pentadecane, n-Hexadecane, and n-Heptadecane at Pressures of 1.5–9 MPa and Temperatures of 303–633 K─Determined with a Raman Spectroscopy-Coupled Microfluidic Device

Fechter

Braeuer

2023

J. Chem. Eng. Data

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Vapor–liquid equilibria (VLE) of binary systems consisting of nitrogen and an n-alkane (n-tridecane, n-pentadecane, n-hexadecane, or n-heptadecane) were measured at a temperature range of T = 303–633 K and pressure range of p = 1.5–9 MPa. A microcapillary setup was employed, which was coupled with an in situ Raman spectroscopy unit. Calibration was performed inside the unsaturated vapor and liquid phase regime in order to link the Raman signal ratios of the saturated vapor and liquid phase spectra to mole fractions. Regarding the Raman evaluation of the saturated vapor phase spectra, a signal contribution from a liquid film can be successfully corrected by an indirect hard modeling (IHM) approach. The obtained experimental data of the binary system n-hexadecane/nitrogen were validated with the available literature data, where good agreement was found. By comparing the saturated compositions between the different binary systems, only small differences could be observed for the saturated liquid phase, while the compositions of the saturated vapor phase clearly deviated from each other. Moreover, the experimental results of all binary systems were compared with the calculated data obtained from different models: the predictive Peng-Robinson equation of state (PPR78) and the perturbed-chain statistical associating fluid theory (PC-SAFT). The data can be well described by the models, with the absolute average deviation (AAD) that is generally higher for the saturated vapor phase compared with the saturated liquid phase.

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Section: Resultsmentioning

confidence: 96%

Section: Vle Datamentioning

confidence: 99%

Vapor–Liquid Equilibria of Binary Systems Consisting of Nitrogen and n-Tridecane, n-Pentadecane, n-Hexadecane, and n-Heptadecane at Pressures of 1.5–9 MPa and Temperatures of 303–633 K─Determined with a Raman Spectroscopy-Coupled Microfluidic Device

Fechter

Braeuer

2023

J. Chem. Eng. Data

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“…XGBoost includes several parameters, making it a complicated model. In addition, hyperparameters are required to limit the danger of over-fitting and forecast variability 47 . The number of iterations (n estimators) and the learning rate are the two key hyper-parameters that avoid overfitting in XGBoost.…”

Section: Model Developmentmentioning

confidence: 99%

Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques

Hadavimoghaddam,

Rozhenko,

Mohammadi

et al. 2023

Sci Rep

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Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting—CatBoost, Gaussian Process Regression—GPR, Extreme Gradient Boosting—XGBoost), and a white-box approach (Genetic Programming—GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R2) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices.

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“…To enable general and thermodynamically consistent predictions, one approach is to predict thermodynamic model parameters [10,11,12,13,14], which also enables simple use in existing process simulation packages. Given the predicted parameters, the thermodynamic model can in turn be used to predict thermodynamic properties.…”

Section: Introductionmentioning

confidence: 99%

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

Felton,

Rasßpe-Lange,

Rittig

et al. 2023

Preprint

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The Perturbed Chain Polar Statistical Associating Fluid Theory (PCP-SAFT) equation of state (EoS) is widely used to predict fluid-phase thermodynamics, but parameterization of PCP-SAFT for individual molecules is often challenging. We propose a machine learning framework called ML-SAFT for predicting parameters of PCP-SAFT. In order to provide data for training machine learning models, we created the largest dataset of regressed PCP-SAFT parameters in the literature. We then conducted extensive evaluation of several machine learning architectures for predicting PCP-SAFT parameters. We found that our best model provided accurate predictions for a wider range of molecules than existing predictive techniques with 39 \% average absolute deviation (AAD) in vapor pressure predictions and 9 \% AAD in density predictions.

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

Cited by 17 publications

References 100 publications

Vapor–Liquid Equilibria of Binary Systems Consisting of Nitrogen and n-Tridecane, n-Pentadecane, n-Hexadecane, and n-Heptadecane at Pressures of 1.5–9 MPa and Temperatures of 303–633 K─Determined with a Raman Spectroscopy-Coupled Microfluidic Device

Vapor–Liquid Equilibria of Binary Systems Consisting of Nitrogen and n-Tridecane, n-Pentadecane, n-Hexadecane, and n-Heptadecane at Pressures of 1.5–9 MPa and Temperatures of 303–633 K─Determined with a Raman Spectroscopy-Coupled Microfluidic Device

Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

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