Thermodynamic Properties Investigation of Process Volatile Organic Compounds (VOCs) and Its Transport Impact Factor in Oil Sands Management
Bibliographic record
Abstract
This paper presents a new approach for the determination of volatile organic compounds (VOCs) characteristics and their migration influencing factors in oil sands management processes and reveals the relationship between different asphaltene content and different solvents. Specifically, thermodynamic (i.e., partitioning coefficients, Kr, specific retention volume, Vg, the activity coefficients, γ and enthalpy of solution, ΔH0) and their impact factors are discussed. Gas-liquid chromatography (GLC) experimental measurements were used as the test data. A range of solvents (nC5, iC5, nC6, nC7, and Toluene) has been tested in different asphalt contents (0, 2.56, 9.93, 36.86, 53.67 wt%). There are temperatures in the range of 333.2–393.2 K (with 10 K increase) were conducted, respectively. The dynamics properties of asphalt mixture are calculated, and the relation between dynamics properties of asphalt mixture and absolute temperature, asphalt content and solvent type is discussed. The results show that within the acceptable error range, partitioning coefficients, Kr, specific retention volume, Vg, and enthalpy of solution, ΔH0 and other thermodynamic properties have a good tendency to predict, they decrease with the increase in asphaltene content and temperature and increase with the increase in solute carbon number.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".