Rapid Determination of Bitumen Content in Athabasca Oil Sands by Laser-Induced Breakdown Spectroscopy
Bibliographic record
Abstract
Evaluation of bitumen content in oil sands feedstock is important to control the extraction process and improve the recovery efficiency. Current standard techniques for bitumen determination in oil sands ore suffer whether from their time-consuming labor intensive method of sample preparations or from requiring a high number of samples for calibration purposes. The analysis of bitumen ore samples is very challenging for the analytical chemist due to its nature of several phases of wet and dry particles and heterogeneous mixture of clay, bitumen, water, and solid contents. In this paper, we present a new enabling method using LIBS to determine rapidly and without sample preparation the bitumen content in oil sands ores. A qualitative study by principal component analysis was first done, and then a partial least-squares method was performed to assess the feasibility of determining bitumen by LIBS. The results show a good correlation between LIBS spectra and bitumen content and a prediction averaged absolute error around 0.7%. This method demonstrates that the LIBS is a promising tool for rapidly assessing oil sands ore grades either in the lab, at-line, or online. Elemental composition of solids is also investigated.
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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 | 0.000 |
| 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".