Application of the Modified Regular Solution Model to Crude Oils Characterized from a Distillation Assay

Ramos-Pallares, F.; Santos, Danielle Freitas; Yarranton, Harvey W.

doi:10.1021/acs.energyfuels.0c01803

Cited by 9 publications

(10 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The n -alkane reference solubility parameters of the SCN pseudocomponents with carbon numbers higher than 5 at the standard conditions of 25 °C and 0.1 MPa were calculated from the following correlation: where T b is the normal boiling point in K. The normal boiling points of n -alkanes were correlated to their molecular weights as follows: …”

Section: Results and Discussion: Model Updatementioning

confidence: 99%

Asphaltene Precipitation from Heavy Oil Diluted with Petroleum Solvents

et al. 2021

Self Cite

View full text Add to dashboard Cite

Heavy oils are sometimes diluted with petroleum solvents for pipeline transportation, and it is necessary to ensure that the selected solvent does not precipitate asphaltenes from the oil. The modified regular solution (MRS) model can predict asphaltene precipitation from heavy oil diluted with pure n-alkanes but has not yet been applied to petroleum solvents. To do so, asphaltene precipitation data were measured for bitumen diluted with petroleum solvents including gas condensates, diesels, kerosene, and naphtha and their blends with n-heptane. The petroleum solvents were characterized into single carbon number (SCN) fractions on the basis of gas chromatography assays. New correlations were proposed for the molecular weight, density, and solubility parameters of the SCN fractions. The MRS model with the characterization and correlations matched asphaltene precipitation data with an average absolute deviation of 0.7 wt % for petroleum solvents with relatively low contents of pure aromatic and cyclic components (<3 wt %), that is, condensates, diesels, and kerosenes. The model failed to match asphaltene precipitation data from the naphtha which contained a significant fraction of pure aromatic and cyclic components (>10 wt %). Hence, the proposed model is currently limited to solvents similar to condensates, diesels, and kerosenes.

show abstract

Section: Results and Discussion: Model Updatementioning

confidence: 99%

Asphaltene Precipitation from Heavy Oil Diluted with Petroleum Solvents

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…16,17 Since petroleum is a mixture of many species, it is more appropriate to refer to the range of solubility parameters between the lowest (δ min ) and highest (δ max ) rather than just a single value. 18 The threshold for Δδ can be exceeded with respect to either δ min or δ max , depending on the direction of the change in the solubility parameter. In the case of asphaltenes, the separation follows on changing the bulk liquid solubility parameter to a lower value, and it is the material at δ max that would first exceed the threshold Δδ.…”

Section: Introductionmentioning

confidence: 99%

Is Solubility Classification a Meaningful Measure in Thermal Conversion?

Jaramillo

Klerk

2022

Energy Fuels

View full text Add to dashboard Cite

Solubility classification is ubiquitous in the description of petroleum production and downstream processing. Flow assurance, phase stability, and oil compatibility are related to the asphaltenes content of petroleum. Solvent classification is also applied to thermal conversion, where the n-alkane insoluble asphaltenes fraction is claimed to be the precursor leading to phase separation and formation of coke. The assumption was questioned that asphaltenes as a solubility class, which was separated at a temperature well below that of the thermal conversion process, would be descriptive of phase separation and conversion at reaction conditions. It led to a more general investigation to evaluate if solvent classification was a meaningful measure in thermal conversion. For this purpose, Athabasca bitumen was solvent separated into n-heptane soluble maltenes and n-heptane insoluble asphaltenes, which together with the bitumen was employed to prepare feed materials with different asphaltenes content. Thermal conversion was performed at 380 °C and different equivalent residence times. The thermally converted products were characterized in terms of n-heptane and CS2 solubility fractions, as well as physical and chemical properties. It was found that formation of CS2 insoluble coke was unrelated to the formation or amount of n-heptane insoluble asphaltenes. It was unlikely that coke formed exclusively from the conversion of asphaltenes. Only the refractive index (reported to be a predictor for onset of asphaltenes phase separation) and free radical content were related to the amount of asphaltenes in the thermally converted products. At the same time, the relationship between asphaltenes and aromatic carbon content was poor, as was the relationship to other properties, such as density, aromatic hydrogen content, and cumulative amount distilled. Solubility classification was not found to be a meaningful measure in thermal conversion, and it was poorly related to properties that could serve as predictors for the reaction chemistry.

show abstract

“…In contrast, adding paraffinic solvents (e.g., n -butane, n -hexane, and n -heptane) leads to asphaltene precipitation. 6 Hence, the nature of solvent and its composition for asphaltene deposition prevention is very important. For establishing this aim, Khormali et al 13 introduced a new chemical solvent package for removing asphaltene from the system.…”

Section: Introductionmentioning

confidence: 99%

Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models

et al. 2022

View full text Add to dashboard Cite

Deasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data pre-processing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C 5 asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery.

show abstract

Application of the Modified Regular Solution Model to Crude Oils Characterized from a Distillation Assay

Cited by 9 publications

References 61 publications

Asphaltene Precipitation from Heavy Oil Diluted with Petroleum Solvents

Asphaltene Precipitation from Heavy Oil Diluted with Petroleum Solvents

Is Solubility Classification a Meaningful Measure in Thermal Conversion?

Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models

Contact Info

Product

Resources

About