RaPId - A Parameter Estimation Toolbox for Modelica/FMI-Based Models Exploiting Global Optimization Methods

Podlaski, Meaghan; Vanfretti, Luigi; Bogodorova, Tetiana; Rabuzin, Tin; Baudette, Maxime

doi:10.1016/j.ifacol.2021.08.391

Cited by 2 publications

(1 citation statement)

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“…For this purpose, two approaches, GlobalSearch and MultiStart, are adopted to find the best optimum. These methods have been widely applied in complex optimization problems to find global or multiple minima. , To the best of our knowledge, we have not found any literature using these GO methods in the thermodynamic area. Each method has advantages and disadvantages.…”

Section: Modeling Approach and Theorymentioning

confidence: 99%

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%