Optimizing XRF Calibration Protocols for Elemental Quantification of Mineral Solids from Athabasca Oil Sands
Bibliographic record
Abstract
As world reserves of conventional oil keep decreasing, there is greater incentive to further develop the Athabasca oil sands of Alberta (Canada). Oil sands being composed of coarse sand, silt and clay solids (80-85%), bitumen (5-15%) and water (1-5%), complete extraction of bitumen from such heterogeneous mixtures is not easy. Studies have shown the adverse effects of some types of mineral solids on bitumen recovery. Quantitative analysis of the elemental composition of mineral solids in ores is then of great importance to the oil sands industry. Two calibration protocols were developed here and implemented for accurate determination of major and minor elements in oil-sand solids by wavelength dispersive X-ray fluorescence (WDXRF) analysis using a fusion-based procedure. Commercially available standards do not span the ranges of element concentrations found in the mineral solids from oil sands. As such, calibration standards for seventeen elements were then designed by mixing pure synthetic oxides or geological reference materials, in order to mimic the elemental concentrations of oil-sand solids fractions. Measurement conditions were optimized to ensure best signal-to-background ratio and minimum line overlap. The limit of detection, calibration ranges and uncertainty errors of the resulting calibration curves are reported, showing excellent precision and accuracy even without matrix-effects correction. Application to analyze a suite of oil sands samples showed that the elemental concentrations of Ti and Zr in problematic solids components correlated well with the concentration of these elements in the entire mineral solids content present in the ores. This observation might be relevant for the development of elemental compositions-based processability markers to identify problem ores yielding poor extraction performance in commercial operations.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".