<i>In Situ</i> Observation of Fouling Behavior under Thermal Cracking Conditions: Hue, Saturation, and Intensity Image Analyses
Bibliographic record
Abstract
Thermal cracking reactions of a variety of heavy petroleum feeds were analyzed by in situ cross-polarized microscopy using a reactor equipped with a sapphire window. Cross-polarized microscopy is a powerful technique for detecting the formation of anisotropic domains of carbonaceous material (mesophase coke). The present study, however, focused on the characterization of the chemical and physical events prior to the formation of new phases by evaluating the changes in image properties with reaction time. Results showed that changes in image brightness were strongly correlated to the conversion of 524+ °C material but could not provide much insight regarding the stability of reacting oils. Color analyses of the cross-polarized micrographs, however, revealed a consistent red-to-blue color shift around the point of instability. This color shift corresponded to either the formation of a fouling layer of isotropic material or a homogeneous color change of the reacting medium, indicating the formation of CS 2 -insoluble material. In both cases, the beginning of the color shift preceded the formation of mesophase, whose appearance had the effect of enhancing the blue color of the samples. The detection of the red-to-blue color shift in reacting samples under thermal cracking conditions provides an improved framework for testing the fouling propensity of feeds or for developing online sensors operating on industrial units.
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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) | 0.003 | 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".