Monte Carlo Simulation of Asphaltenes and Products from Thermal Cracking
Bibliographic record
Abstract
Analytical data from the characterization of the liquid products from the thermal cracking of n -C 7 Cold Lake asphaltenes were transformed to probability density functions (PDFs) that described the molecular weight distributions of the asphaltene building blocks. These distributions were used for Monte Carlo construction of populations of 10 000 asphaltene molecules, constrained by an experimental molecular weight distribution and the yield of products boiling below 538 °C from cracking in the presence of hydrogen and iron sulfide. The resulting distribution of asphaltene molecules contained between 1 and 15 building blocks, with a mode of 4. The mode of the distribution was insensitive to the fraction of the asphaltenes that consisted of large building blocks, boiling above 538 °C. Breakage of the bonds between the building blocks, to simulate cracking, gave yields of product fractions and boiling curves that were consistent with the experimental data. The relatively small number of building blocks in the majority of the asphaltene molecules suggests that molecular topology is not significant in the cracking reactions. Simulation of the cracking reactions linear versus dendritic molecular topology did not show significant differences in yield of product fractions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".