Comprehensive Two-Dimensional Gas Chromatography for Fast Screening of Wash Oils
Bibliographic record
Abstract
Fast screening of wash oils is demonstrated using comprehensive two-dimensional gas chromatography (GCxGC). Wash oils are used in ethylene production plants to minimize compressor fouling. The composition of a wash oil determines its effectiveness in solubilizing heavy hydrocarbons. In particular, the relative amount of 1- and 2-ring aromatics is important. The presence of oxygenates is undesirable because of adverse effects to the process. It is shown that GCxGC is well suited for this application. Species in wash oils are separated and grouped into three bands: a nonpolar aliphatics band, 1- and 2-ring aromatics band, and polyaromatics band. For a given polar secondary column, the spacing between bands in the second dimension can be adjusted in a broad range by selecting a primary column and an oven-temperature-programming rate. Integration of GCxGC peaks is evaluated using a standard GC integration program and a new GCxGC integration program. Consistent results are obtained using both programs for well-separated GCxGC peaks with relative differences for individual peak ranging from 0.04% to 1.6%. Peak responses are integrated by the GCxGC software, and the relative amounts of aromatics content and aliphatics content are estimated by peak response percent with relative standard deviations ranging from 0.15% to 2.8% (n = 3).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".