Multiscale modelling of asphaltene disaggregation
Bibliographic record
Abstract
Asphaltene aggregation reduces bitumen upgrading efficiency by increasing bitumen viscosity and coke formation. Our approach to model asphaltene aggregation involves geometry optimisation by using the Harris approximation implemented in DMol3, followed by a solvation calculation by using the three-dimensional molecular theory of solvation (a.k.a. 3D-RISM) we have developed recently [A. Kovalenko, Three-dimensional RISM theory for molecular liquids and solid–liquid interfaces, in Understanding Chemical Reactivity: Molecular Theory of Solvation, F. Hirata ed., Vol. 24, Kluwer Academic Publishers, New York, NY, 2003, pp. 169–275]. From the Harris approximation, we obtain the Hirshfeld and Mulliken asphaltene atomic charges. The 3D-RISM theory allows one to model solvation at given temperature, solvent density and solvent composition. The theory predicts solvation structure and thermodynamic characteristics, such as the potential of mean force (PMF). We investigate the effect of the Hirshfeld and Mulliken charge calculation methods on the PMF values for asphaltene disaggregation in quinoline and 1-methylnaphthalene solvents at 298 and 473 K. Our PMF results predict that asphaltene disaggregation is favoured in quinoline at 473 K, whereas in 1-methylnaphthalene the asphaltene aggregate would remain undisturbed. These results are in agreement with the experiment and correlate with the molecular dynamics (MD) simulation results [T. Takanohashi, S. Sato, and R. Tanaka, Structural relaxation behaviors of three different asphaltenes using MD calculations, Petr. Sci. Technol. 22 (2004), pp. 901–904]. The statistical–mechanical 3D-RISM method probes the entire phase space and yields the solvation structure and thermodynamics at a much lower computational cost than MD, and thus gives access to solvation processes that occur on large time and space scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".