Experimental Evaluation of Volatile Organic Compound Quantification Methods for Reciprocating Natural Gas Engines
Bibliographic record
Abstract
Abstract Compressor stations utilizing large-bore natural gas engines transport natural gas through pipelines worldwide. One emission class regulated by the Environmental Protection Agency (EPA) is volatile organic compounds (VOCs), which are nonmethane, nonethane, nonaldehyde hydrocarbons. The combination of a gas chromatograph (GC) and a flame ionization detector (FID) can measure VOCs, following EPA Method 18/25A. The Fourier transform infrared spectrometer (FTIR) also measures VOCs, following EPA Method 320. Multiple VOC calculation techniques are utilized, some combining measurements from separate analyzers. Two basic methods of extracting exhaust gas are direct extraction and Tedlar bag sampling. In this study, various VOC quantification methods are evaluated. Exhaust gas was sampled from a Cooper-Bessemer GMV lean-burn engine and a Caterpillar G3304 rich-burn engine. The GMV was tested in three configurations: open chamber spark ignition, precombustion chamber (PCC) ignition, and PCC ignition with high-pressure fuel injection. Ignition timing sweeps were performed on both engines, and a fuel variability test was performed on the GMV. Results showed that the Gasmet and MKS FTIRs’ (Method 320) VOC measurements deviate significantly from the HP GC when measuring low molar concentrations, albeit below regulatory limits. A common VOC quantification approach is subtracting the sum of methane and ethane FTIR measurements from a total hydrocarbon measurement utilizing a FID. This method produces uncertainties of 190% and overestimates VOC concentration by an average of 100%. The Tedlar bag sampling method produced VOC measurements within −2% of the direct extraction method.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".