Modelling of a Naphtha Recovery Unit (NRU) with Implications for Process Optimization

Du, Jiawei; Cluett, William R.

doi:10.3390/pr6070074

Cited by 5 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When bitumen is extracted from oil sand and a naphthenic process is employed for froth treatment, the Naphtha Recovery Unit (NRU) is employed to recover naphtha from the tailings, for reuse in the process and to reduce the environmental impact of the process. This is an energy-intensive step, with environmental guidelines for naphtha recovery are required to be met [4]. Therefore, the thermodynamics for highly diluted hydrocarbon in water systems is of particular interest.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

et al. 2021

View full text Add to dashboard Cite

The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

show abstract

Section: Introductionmentioning

confidence: 99%

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Bitumen can be blended with a solvent to remove water and solids. Usually, this solvent is a locally produced naphtha [4]. Following this process, the naphtha can be reclaimed in a Naphtha Recovery Unit (NRU).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends

Lopez-Ramirez,

Lopez-Zamora,

Escobedo

et al. 2023

Processes

View full text Add to dashboard Cite

Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R2 Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has a major influence on ANN vapour pressure predictions, while the concentration of octane, the naphtha surrogate having, in contrast, a lesser effect. Furthermore, the ANN data obtained allows the calculation of octane-in-water and water-in-octane maximum solubilities.

show abstract

“…In the naphtha recovery unit (NRU) process, naphtha is recovered from the froth treatment process tailings for reuse, while the remaining fraction is discharged to the tailings pond. [4] This process is energy-intensive, and environmental guidelines for naphtha recovery must be met. [4] The simulation of the NRU requires knowledge of the unit thermodynamics involved.…”

Section: Introductionmentioning

confidence: 99%

“…[4] This process is energy-intensive, and environmental guidelines for naphtha recovery must be met. [4] The simulation of the NRU requires knowledge of the unit thermodynamics involved. One of the main issues to clarify in naphtha-water systems is the transition from the three-phase vapour-liquid-liquid equilibrium (VLLE) to the two-phase vapour-liquid equilibrium (VLE).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Modelling of a Naphtha Recovery Unit (NRU) with Implications for Process Optimization

Cited by 5 publications

References 9 publications

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends

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

Contact Info

Product

Resources

About