<em>In Situ</em> Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions
Bibliographic record
Abstract
To help address production issues in refineries caused by the fouling of process units and lines, we have developed a setup as well as a method to visualize the behavior of petroleum samples under process conditions. The experimental setup relies on a custom-built micro-reactor fitted with a sapphire window at the bottom, which is placed over the objective of an inverted microscope equipped with a cross-polarizer module. Using reflection microscopy enables the visualization of opaque samples, such as petroleum vacuum residues, or asphaltenes. The combination of the sapphire window from the micro-reactor with the cross-polarizer module of the microscope on the light path allows high-contrast imaging of isotropic and anisotropic media. While observations are carried out, the micro-reactor can be heated to the temperature range of cracking reactions (up to 450 °C), can be subjected to H2 pressure relevant to hydroconversion reactions (up to 16 MPa), and can stir the sample by magnetic coupling. Observations are typically carried out by taking snapshots of the sample under cross-polarized light at regular time intervals. Image analyses may not only provide information on the temperature, pressure, and reactive conditions yielding phase separation, but may also give an estimate of the evolution of the chemical (absorption/reflection spectra) and physical (refractive index) properties of the sample before the onset of phase separation.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".