Development of a Population Balance Model to Describe the Influence of Shear and Nanoparticles on the Aggregation and Fragmentation of Asphaltene Aggregates
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Bibliographic record
Abstract
The precipitation and deposition of asphaltenes is a primary problem related to the processing, transportation, and production of oil. Flocculation of asphaltene aggregates is likely to occur during the production and processing of crude oil. Recently, it has been shown that nanotechnology in the form of nanoparticles is useful for the inhibition or prevention of asphaltene formation damage. Although it is well-known that the adsorption of asphaltenes on the nanoparticle surface would reduce the capacity of these asphaltic compounds to interact with each other, limited studies have been performed regarding the processes and the mechanisms associated with the effect of nanoparticles on the inhibition of the formation damage due to asphaltenes. To better understand this phenomenon from a mathematical approach, a population balance model (PBM) is proposed to describe the kinetics of asphaltene flocculation-fragmentation in the presence of nanoparticles. The model assumes that asphaltenes in the presence of a shear rate are related to the aggregation and fragmentation phenomena and includes a term related to the asphaltene adsorption on nanoparticles. An adsorption kinetic term was introduced into the model using the double exponential model. Experimental data of the kinetics of asphaltene aggregation were obtained by dynamic light scattering (DLS) measurements at a fixed initial asphaltene concentration of 1000 mg/L and with different Heptol mixtures. In this study, commercial silica, γ-alumina, and magnetite nanoparticles were used as adsorbents to study the effect of the chemical nature of the nanoparticles on the inhibition of the asphaltene growth and for model validation. Additionally, to demonstrate the versatility of the proposed model, the effect of asphaltene was also evaluated. The obtained results from the proposed population balance model agree well with the experimental data, within an RSME % < 9%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it