Determination of the permittivity of <i>n</i>‐hexane/oil sands mixtures over the frequency range of 200 MHz to 10 GHz
Bibliographic record
Abstract
Abstract Combined electromagnetic (EM) heating and solvent injection has been recently proposed to recover bitumen from oil sands due to its great environmental friendliness. The permittivity of oil sands with the presence of solvent is a crucial property for the design, evaluation, and optimization of this process. In this study, we use the open‐ended coaxial probe method to measure the permittivity of oil sands over the frequency range of 200 MHz to 10 GHz. Results of the permittivity of the constituents of oil sands reveal that water is a major dielectric contributor; no relaxation phenomenon has been found for bitumen, sand, and n‐hexane over the tested frequency range. Also, the results for the oil sands mixtures show that water content is crucial for the permittivity of oil sands; both the dielectric constant and loss factor are enhanced with an increasing water content. With the addition of n‐hexane, the permittivity of bitumen slightly changes and fluctuates between the dielectric values of pure bitumen and n‐hexane. As for the n‐hexane/oil sands mixtures, the added n‐hexane induces the asphaltene aggregation/flocculation, affecting the interaction between water and asphaltene and leading to an enhanced free water content in the oil sands. Consequently, the permittivity of oil sands significantly increases after n‐hexane addition. Based on the experimental data, we evaluate the prediction accuracy of commonly used mixing models. A modified Lichtenecker‐Rother model considering effective water saturation is proposed to accurately characterize the permittivity of n‐hexane/oil sands mixtures. The obtained data and correlations can be useful when experimental data are missing.
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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 | 0.000 | 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 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".