Optimizing Paraffin and Naphthene Wax-Treatment Options Using Cross-Polarized Microscopy
Bibliographic record
Abstract
Abstract Wax deposition is a worldwide problem for the upstream petroleum industry. Considerable resources are expended every year on trial-and-error type chemical treatment options. In a laboratory setting chemical treatments are often optimized using viscosity and pour point measurements. Viscometry and pour points can only measure bulk properties. Cross-Polarized Microscopy (CPM), however, has been shown in previous work to be a useful tool to determine individual wax crystal size and morphology. Thus, in this work CPM was used to evaluate the effectiveness of wax inhibitor treatments for paraffinic and naphthenic base oils by monitoring the morphology and size of the wax crystals before and after the application of the chemical treatments. It has been demonstrated that there is a statistically significant reduction in wax crystal size after the wax treatment. Furthermore, the observations of the wax morphologies through CPM have demonstrated that the chemical treatment effectively inhibits wax crystal growth for both macrocrystalline (paraffin) and microcrystalline (naphthene and iso-paraffin) waxes. In addition to viscosity and pour point measurements CPM has been demonstrated to be a valuable tool to the optimization of wax-treatment options.
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 | 0.000 | 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) | 0.001 | 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".