Hydrogen-Deuterium chemical exchange in supercritical water: Thermodynamic considerations for optimizing the synthesis of high degree deuterated benzene

Sadjadi, Sodeh; Hosseinpour, Morteza; Mohammadnezhad, Farrokh; Ahmadi, Seyed Javad; Khazayi, Mohamad Ali

doi:10.1016/j.supflu.2017.02.010

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…Given the reported advantages of using the Kabadi-Danner modification with the Soave-Redlich-Kwong EoS (SRKKD EoS) for VLL equilibrium calculations for hydrocarbonwater blends [12,13], the SRKKD EoS was used in the present study. The SRKKD EoS can be defined by Equations ( 4)-( 8) as follows:…”

Section: Mathematical Modelmentioning

confidence: 99%

A Machine Learning Approach for Phase-Split Calculations in n-Octane/Water and PASN/Water Systems

2022

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Flash calculations, including phase split and phase classification for both n-octane/water blends and paraffinic aromatic synthetic naphtha (PASN)/water blends present significant computational challenges. Calculations to establish the two-phase and three-phase regions, as well as the transitions between regions, were addressed by a phase classification method proposed in a recent contribution involving machine learning (ML). This work focusses on the phase-split calculations, considering (a) the lack of numerical convergence of the traditional calculations and their related numerical issues for water/n-octane and PASN/water systems based on the Rachford–Rice derived surfaces and (b) the successful implementation of an ML approach based on a K-nearest-neighbor (KNN) algorithm, which uses the abundant experimental data obtained in a CREC-VL cell.

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Section: Mathematical Modelmentioning

confidence: 99%

A Machine Learning Approach for Phase-Split Calculations in n-Octane/Water and PASN/Water Systems

2022

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“…Given the reported advantages of using the SRKKD EoS for VLLE calculations in the case of hydrocarbonwater blends, [12,13] SRKKD EoS was used to describe the hydrocarbon-water blends of this study. The SRKKD EoS can be defined by Equations ( 1)-( 5) as follows:…”

Section: Vlle Using a Srkkd Eosmentioning

confidence: 99%

“…Given the reported advantages of using the SRKKD EoS for VLLE calculations in the case of hydrocarbon‐water blends, [ 12,13 ] SRKKD EoS was used to describe the hydrocarbon‐water blends of this study. The SRKKD EoS can be defined by Equations () as follows:

P = \frac{RT}{V - b} - \frac{a}{V ((), V + b)}

a = 0.427 48 \frac{R^{2} T_{C}^{2}}{P_{C}} {[1 + Ω (1 - T_{r}^{1 / 2})]}^{2}

b = 0.086 64 \frac{R T_{C}}{P_{C}}

normalΩ = 0.480 + 1.574 normalω - 0.176 ω^{2}

Z^{3} - Z^{2} + ((), A^{'} - B^{'} - {B^{'}}^{2}) Z - A^{'} B^{'} = 0

Regarding the mixing rules as needed for the SRK EoS parameters, they are given by Equations () and (), with the Kabadi‐Danner modification reported by Equations () and () [ 10 ] :

a_{mix} = a_{0} + a_{KD}

a_{0} = \sum_{i = 1}^{N} \sum_{j = 1}^{N} x_{i} x_{j} ((), 1 - k_{ij}) \sqrt{a_{i} a_{j}}

...…”

Section: Mathematical Formulationmentioning

confidence: 99%

Vapour–liquid–liquid and vapour–liquid equilibrium of paraffinic aromatic synthetic naphtha/water blends: Prediction of the number of phases

et al. 2021

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Bitumen is extracted from oil sands using warm water and additives. The resulting bitumen froth is diluted with naphtha in a froth treatment process. Residual naphtha in the aqueous tailings of the froth treatment unit is recovered in a naphtha recovery unit (NRU). It is imperative to maximize the naphtha recovery process to minimize the plant's environmental and economic impact. It is, in this respect, that NRU vapour–liquid–liquid equilibrium data is of significant value. In this work, a paraffinic‐aromatic synthetic naphtha (PASN) with a true boiling point (TBP) similar to that of froth treatment naphtha is used. Water/PASN mixtures are studied using the Soave‐Redlich‐Kwong equation of state with a Kabadi‐Danner modification. The tangent plane distance (TPD) is evaluated as a possible criterion to calculate the number of phases, with its significant shortcomings being established. As well, experimental data obtained in a CREC‐VL‐Cell is observed to display higher solubilities of PASN in water than the ones obtained by HYSYS‐Aspen Plus V9 simulation. To address these issues, a machine learning (ML)‐based phase classification methodology was considered, predicting the number of phases with a 99% recall. This anticipates that ML will be of significant value for faster convergence of the flash split calculations for the naphtha hydrocarbon‐water systems under consideration.

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The synergistic effect between supercritical water and redox properties of iron oxide nanoparticles during in-situ catalytic upgrading of heavy oil with formic acid. Isotopic study

Hosseinpour

Fatemi

Ahmadi

et al. 2018

Applied Catalysis B: Environmental

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Hydrogen-Deuterium chemical exchange in supercritical water: Thermodynamic considerations for optimizing the synthesis of high degree deuterated benzene

Cited by 9 publications

References 32 publications

A Machine Learning Approach for Phase-Split Calculations in n-Octane/Water and PASN/Water Systems

A Machine Learning Approach for Phase-Split Calculations in n-Octane/Water and PASN/Water Systems

Vapour–liquid–liquid and vapour–liquid equilibrium of paraffinic aromatic synthetic naphtha/water blends: Prediction of the number of phases

The synergistic effect between supercritical water and redox properties of iron oxide nanoparticles during in-situ catalytic upgrading of heavy oil with formic acid. Isotopic study

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