A computational tool for parameter estimation in EoS: New methodologies and natural gas phase equilibria calculations

Abunahman, Samir Silva; Santos, Letícia Cotia dos; Tavares, Frederico W.; Kontogeorgis, Georgios M.

doi:10.1016/j.ces.2019.115437

Cited by 8 publications

(5 citation statements)

References 51 publications

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“…The general framework of the proposed optimization technique and algorithm for flash calculation is adapted from Abunahman et al (2020) and Sabbagh (2005), respectively. The process starts with having the composition and molecular weights of the different fractions/components of the mixture (Figure ).…”

Section: Modeling Approach and Theorymentioning

confidence: 99%

“…Therefore, the objective function(s) (F(X)), defined here from the thermodynamic model, are minimized, subject to the restrictions h(X). It is worth mentioning that the accuracy of the tuning procedure is highly dependent on the choice of the objective function and optimization approach . For this purpose F ( X ) = ∑ k n p [ 1 n e ∑ i n e false( y k , i e − y k , i p false) 2 σ k , i 2 ] where y, n, and σ represent the variable, the number of variables, and the variance of the experimental variables, respectively.…”

Section: Modeling Approach and Theorymentioning

confidence: 99%

“…The arbitrated algorithm used by Abunahman et al (2020) uses PSO results as the initial estimation of the method. In contrast, the PSO method is not robust enough to locate the global optima of the problem . Thus, the current research mainly focused on selecting the initial guess of the adjustable variables within the predefined regions of the optimization basins.…”

Section: Modeling Approach and Theorymentioning

confidence: 99%

“…The superscripts e and p stand for experimental and model variables. The experimental variables can replace the variance value due to the unavailability of this parameter with experimental data used for thermodynamic modeling since the experimental data might be limited or difficult to reproduce …”

Section: Modeling Approach and Theorymentioning

confidence: 99%

“…The density and precipitation experimental data (asphaltene yield or onset) can improve the EoS model. Yet, the optimization approach for parameter estimation can significantly advance the correlation/prediction ability of the EoS . The importance of optimization is discussed in the modeling section of the current study.…”

Section: Introductionmentioning

confidence: 99%

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A Global Optimization Approach for Parameter Estimation of Cubic Plus Association Equations of State in Asphaltene Modeling: Studying the Temperature, Solvent, and Binary Interactions

Alimohammadi

James

2022

Energy Fuels

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Equation of state (EoS) modeling of complex molecular chain asphaltenes suffers from uncertainty about adjusting the parameters and intermolecular forces. Better accounting of the association of hydrogen bonding and the impact of van der Waals forces could significantly improve EoS models compared to traditional methods. Our approach is to improve the procedure for estimating parameters (e.g., binary interaction parameters and association energy) and simplifying the characterization approach to improve EoS models. In this study, cubic plus association (CPA) EoS was employed to calculate asphaltene precipitation yield and onset under different conditions, including temperature, the addition of n-alkane (nC 5 −nC 12 ), and different mixture compositions. A global optimization framework was implemented to estimate the global best fit of the adjustable parameters and skip the local minima. These parameters are the physical and association constants of the CPA EoS and the binary parameters. The oil was treated by the characterization method from Flory−Huggins EoS to simplify the CPA EoS. Additionally, new correlations were also developed to calculate the binary parameters and association used in this work. Comparisons between the results obtained from the globally optimized CPA EoS and experimental data exhibit acceptable agreement (average deviation of less than 0.067 for correlation and prediction). The relative importance analysis was performed using Pearson's correlation coefficient method to show the importance of the operational conditions studied here on asphaltene precipitation and binary interaction parameters. The analysis revealed that the composition of the mixture (dilution ratio) is the most influential factor contributing to the asphaltene precipitation. Additionally, binary interaction parameters are most affected by the carbon number of the n-alkane followed by the temperature. The simplified CPA EoS can be used by other researchers to increase the efficiency of the asphaltene thermodynamic modeling, e.g., industrial application.

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Section: Modeling Approach and Theorymentioning

confidence: 99%