Simultaneous Determination of Dissolved Gases and Moisture in Mineral Insulating Oils by Static Headspace Gas Chromatography with Helium Photoionization Pulsed Discharge Detection
Bibliographic record
Abstract
This paper presents the development of a static headspace capillary gas chromatographic method (HS-GC) for simultaneously determining dissolved gases (H2, O2, N2, CO, CO2, CH4, C2H6, C2H4, C2H2, C3H8) and moisture from a unique 15-mL mineral oil sample. A headspace sampler device is used to equilibrate the sample species in a two-phase system under controlled temperature and agitation conditions. A portion of the equilibrated species is then automatically split-injected into two chromatographic channels mounted on the same GC for their separation. The hydrocarbons and the lighter gases are separated on the first channel by a GS-Q column coupled with a MolSieve 5-A column via a bypass valve, while the moisture is separated on the second channel using a Stabilwax column. The analytes are detected by using two universal pulsed-discharge helium ionization detectors (PDHID). The performance of the method was established using equilibrated vials containing known amounts of gas mixture, water, and blank oil. The signal is linear over the concentration ranges normally found for samples collected from open-breathing power transformers. Determination sensitivity varies with the nature of the species considered with values as high as 21 500 A x 10(-9) s (microg/ g)(-1) for H2O, 46-216 A x 10(-9) s (microL/L)(-1) for the hydrocarbons and carbon oxides, and as low as 8-21 A x 10(-9) s (microL/L)(-1) for the O2 and N2 permanent gases. The detection limit of the method is between 0.08 and 6 microL/L for the dissolved gases, except for O2, N2, and CO2, where higher values are observed due to air intrusion during sampler operations, and 0.1 microg/g for the dissolved water. Ten consecutive measurements in the low and high levels of the calibration curves have shown a precision better than 12% and 6%, respectively, in all cases. A comparison study between the HS-GC method and the ASTM standard procedures on 31 field samples showed a very good agreement of the results. The advantages of configuring the arrangement with two PDHID over the conventional flame ionization and thermal conductivity detectors were clearly demonstrated.
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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".