Application of Chebyshev polynomials to predict phase behavior of fluids containing asphaltene and associating components using SAFT equation of state

Tabatabaei-Nejad, Seyyed Alireza; Khodapanah, Elnaz

doi:10.1016/j.fuel.2010.04.017

Cited by 13 publications

(8 citation statements)

References 19 publications

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“…Statistical associating fluid theory (SAFT) is a more advanced EoS which is used in some modelling studies of asphaltene precipitation. [25][26][27] One of the disadvantages of the EoS based models is their need for fluid characterization generally up to C 30þ . Furthermore, these models sometimes encounter convergence problems particularly when the polydispersity of the asphaltene particles is considered.…”

Section: Eos Based Modelsmentioning

confidence: 99%

“…Li and Firoozabadi applied cubic‐plus‐association (CPA) EoS to study asphaltene precipitation from normal alkane diluted model solution and heavy oil and bitumen. Statistical associating fluid theory (SAFT) is a more advanced EoS which is used in some modelling studies of asphaltene precipitation . One of the disadvantages of the EoS based models is their need for fluid characterization generally up to C 30+ .…”

Section: Introductionmentioning

confidence: 99%

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Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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Section: Eos Based Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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show abstract

“…The bubble curve data is seen to fall on the boundary of a three-phase vapour-liquid-liquid equilibrium (VLLE) region, whereas the asphaltene-precipitation data is shown to be related to the liquid-liquid (LLE) boundary of the diagram. The PC-SAFT equation of state has also shown to be successful to model the phase behaviour of asphaltene mixtures, as demonstrated by the numerous works of Chapman and coworkers [155,[198][199][200][201][202][203][204][205][206][207][208] and others [65,163,[214][215][216]. In most of these studies, the effects of size asymmetry and van der Waals interactions are assumed to dominate the phase behaviour, and association interactions are therefore not considered.…”

Section: Statistical Associating Fluid Theorymentioning

confidence: 99%

Thermodynamic modelling of asphaltene precipitation and related phenomena

Forte

Taylor

2015

Advances in Colloid and Interface Science

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“…The algorithm starts by interpolating the logarithm of the objective function on Chebyshev polynomials (Burden et al, 1981;Tabatabaei-Nejad and Khodapanah, 2010a) in the specified interval to provide a closed form expression for the logarithm of the objective function. The Chebyshev polynomials are then converted to ordinary polynomials to obtain an approximation for the logarithm of the objective function as an ordinary polynomial.…”

Section: Dew Point Pressure(s) Calculation-using Chebyshev Polynomialsmentioning

confidence: 99%

A consistent method for simultaneous calculation of upper and lower dew point pressures of gas condensate fluids

Khodapanah

Tabatabaei-Nejad

2014

Journal of Natural Gas Science and Engineering

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Application of Chebyshev polynomials to predict phase behavior of fluids containing asphaltene and associating components using SAFT equation of state

Cited by 13 publications

References 19 publications

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Thermodynamic modelling of asphaltene precipitation and related phenomena

A consistent method for simultaneous calculation of upper and lower dew point pressures of gas condensate fluids

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