Monitoring Bitumen Upgrading, Bitumen Recovery, and Characterization of Core Extracts by Hydrocarbon Group-type SARA Analysis
Bibliographic record
Abstract
Hydrocarbon group-type analyses are presented in this work for characterization of samples related to the Province of Alberta's abundant bitumen reserves that demand more effective ways of utilization and valorization. The main objective of the study is the evaluation of thin layer chromatography with flame ionization detection (TLC-FID) for rapid hydrocarbon group-type monitoring of bitumen samples and determination of the feasibility of each method for application with very small sample amounts. TLC-FID is a known technique for hydrocarbon (HC) group-type analysis. Different methods of utilization of TLC-FID are assessed here. The analytical techniques employed and data obtained are presented and compared. HC group types are presented in term of saturates, aromatics, resins, and asphaltenes (SARA). Applications are shown for three types of studies related to bitumen: with respect to ultradispersed catalytic bitumen upgrading; solvent-based enhanced bitumen production (Vapex); and characterization of organic extracts from reservoir cores. Techniques are evaluated and validated for each utilization and the preferred methodology indicated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".