Modeling and predicting solubility of n-alkanes in water

Safamirzaei, Mani; Modarress, Hamid

doi:10.1016/j.fluid.2011.06.022

Cited by 25 publications

(17 citation statements)

References 38 publications

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“…Later on, this approach, with one temperature‐dependent and another temperature‐independent binary adjustable parameter, has been implemented for estimating the data in water solutions of nitrogen and hydrogen. Some additional references presenting the modeling of phase behavior in aqueous systems should be acknowledged as well. A particular importance for this research present the studies of Economou and Donohue and Economou and Tsonopoulos .…”

Section: Introductionmentioning

confidence: 99%

Predicting phase behavior in aqueous systems without fitting binary parameters II: Gases and non‐aromatic hydrocarbons

2017

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This investigation continues a series of studies evaluating the capability of the recently proposed CP‐PC‐SAFT and sPC‐SAFT of Liang et al. to estimate the thermodynamic properties of aqueous systems in the entirely predictive manner. Similarly to the previously considered systems, CP‐PC‐SAFT remains a realistic estimator of the available data on critical loci, high pressure‐high temperature phase equilibria and volumetric properties also in the cases of non‐polar gases and non‐aromatic hydrocarbons from argon and nitrogen till n‐eicosane and squalene while keeping zero values of binary parameters. Nevertheless, such application of the model poses certain unavoidable compromises on its accuracy. Inter alia, CP‐PC‐SAFT is a particularly inaccurate estimator of the water‐rich liquid phases away from the critical points. sPC‐SAFT predicts these data in a more reliable manner. Moreover, its predictive capability goes beyond the liquid phases and it exhibits a remarkable accuracy in forecasting various phase equilibria below the critical point of water. © 2017 American Institute of Chemical Engineers AIChE J, 2017

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Section: Introductionmentioning

confidence: 99%

Predicting phase behavior in aqueous systems without fitting binary parameters II: Gases and non‐aromatic hydrocarbons

2017

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“…Each kind of data, which in some cases the relations between them are very complicated, can be modeled via ANN. [41,44,[55][56][57][62][63][64][65][66][67][68][69][70][71][72][73] By using appropriate experimental data which can be obtained from the literature, most physicochemical properties can be calculated by means of ANN. ANN is an especially efficient algorithm which by learning the relationships between input and output vectors approximates any thermophysical properties by utilizing a nonlinear model.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The number of neurons presented in the input and output layer depends on the number of variables. [41,65,66,68,[70][71][72] The performance of ANN is generally based on parameters architecture and setting. One of the most difficult tasks in studying ANN is finding an appropriate architecture.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N_222(n)]Tf₂N) by Means of Artificial Intelligence Techniques

Golzar¹,

Amjad‐Iranagh²,

Modarress³

2014

Journal of Dispersion Science and Technology

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In this work, thermophysical properties of quaternary ammonium-based ionic liquids (ILs) including density, surface tension, and viscosity are produced by two powerful artificial intelligence techniques: genetic function approximation (GFA) and artificial neural network (ANN). In proposed GFA and ANN models, the critical temperature and water content of studied ILs ([N 222(n) ]Tf 2 N with n ¼ 5, 6, 8, 10, and 12) as well as operation temperature were given as the input parameters and the density, surface tension, and viscosity were predicted as the output results. The obtained results reveal that the selected input parameters are appropriate for prediction of thermophysical properties of quaternary ammonium-based ILs. In addition, the high statistical quality represented by various criteria and the low prediction errors of the presented models indicate that they can accurately predict the density, surface tension, and viscosity of new ILs without recourse to experimental data.

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“…ANN is an appropriate method for estimating complex functions in order to evaluate equilibrium and transport properties such as VLE, [24,25] solubility, [26,27] and thermal conductivity [28][29][30] for mixtures. The present study introduces a feed forward ANN model, trained by the Levenberg-Marquardt algorithm [31][32][33], to predict the Fick diffusion coefficient in several liquid systems.…”

Section: Model Names Equationsmentioning

confidence: 99%

Developing a feed forward neural network multilayer model for prediction of binary diffusion coefficient in liquids

Beigzadeh

Rahimi

Shabanian

2012

Fluid Phase Equilibria

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Modeling and predicting solubility of n-alkanes in water

Cited by 25 publications

References 38 publications

Predicting phase behavior in aqueous systems without fitting binary parameters II: Gases and non‐aromatic hydrocarbons

Predicting phase behavior in aqueous systems without fitting binary parameters II: Gases and non‐aromatic hydrocarbons

Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N_222(n)]Tf₂N) by Means of Artificial Intelligence Techniques

Developing a feed forward neural network multilayer model for prediction of binary diffusion coefficient in liquids

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Modeling and predicting solubility of n-alkanes in water

Cited by 25 publications

References 38 publications

Predicting phase behavior in aqueous systems without fitting binary parameters II: Gases and non‐aromatic hydrocarbons

Predicting phase behavior in aqueous systems without fitting binary parameters II: Gases and non‐aromatic hydrocarbons

Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N222(n)]Tf2N) by Means of Artificial Intelligence Techniques

Developing a feed forward neural network multilayer model for prediction of binary diffusion coefficient in liquids

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Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N_222(n)]Tf₂N) by Means of Artificial Intelligence Techniques