Derivation of Molecular Representations of Middle Distillates
Why this work is in the frame
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Bibliographic record
Abstract
The molecular representation of hydrocarbon mixtures is critical to the advanced kinetics modeling of refining conversion processes; however, the achievement of such a representation is considered a significant challenge. The isomeric lump in a homologous series sets the analytical limit in analytical characterization of middle and heavy distillates. This paper proposes a new procedure for de-lumping detailed analytical information generated using a gas chromatography−field-ionization mass spectrometry (GC−FIMS) method into a molecular representation. As a result, concentration distributions of the various molecules in the sample of interest are calculated. This paper presents a deterministic computer-assisted procedure that automatically (i) generates hydrocarbon molecules according to literature-based set of rules, (ii) selects the hydrocarbon molecules that are most thermodynamically stable (likely to exist), (iii) optimizes the three-dimensional geometry of those molecules and calculates their thermodynamic properties, and (iv) calculates the concentration distributions of those molecules. A separate validation calculation involves (i) prediction of the physical properties for pure hydrocarbons using quantitative structure−property relationship (QSPR) correlations; and (ii) prediction of bulk physical properties of this mixture using the calculated concentrations, properties of its pure hydrocarbon components, and suitable mixing rules. This procedure was applied to find molecular representations of five middle-distillate samples and the results were validated through the estimation and comparison of such simulated and measured physical properties as density, refractive index, and simulated distillation curves (the latter of which are used as a consistency check). Good agreement was observed between the predicted and measured properties for all five middle-distillate samples. This agreement validates the molecular characterization algorithm, at least for the purpose of bulk property prediction in middle distillates.
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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 | 0.000 | 0.000 |
| 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