Characterization of Methane Emissions from a Natural Gas-Fuelled Marine Vessel under Transient Operation
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Natural gas is an increasingly attractive fuel for marine applications due to its abundance, lower cost, and reduced CO<sub>2</sub>, NO<sub>x</sub>, SO<sub>x</sub>, and particulate matter (PM) emissions relative to conventional fuels such as diesel. Methane in natural gas is a potent greenhouse gas (GHG) and must be monitored and controlled to minimize GHG emissions. In-use GHG emissions are commonly estimated from emission factors based on steady state engine operation, but these do not consider transient operation which has been noted to affect other pollutants including PM and NO<sub>x</sub>. This study compares methane emissions from a coastal marine vessel during transient operation to those expected based on steady state emission factors.</div><div class="htmlview paragraph">The exhaust methane concentration from a diesel pilot-ignited, low pressure natural gas-fuelled engine was measured with a wavelength modulation spectroscopy system, during periods of increasing and decreasing engine load (between 3 and 90%). Methane concentration, methane emissions, and excess air ratio were compared to steady state conditions. Load increases resulted in similar exhaust methane concentrations relative to steady state values (within 8%). In contrast, decreasing engine load increased the exhaust methane concentration up to 1.9-times relative to equivalent steady-load values and showed total methane emissions up to 43% higher over the transient duration. However, the marine vessel considered here, operated at steady load approximately 91% of the time and the transient CH<sub>4</sub> emissions will have only a negligible impact on the total in-use GHG emission (1.7% increase). This indicates that while transient operation does affect CH<sub>4</sub> emissions, the uncertainty introduced in using steady state emissions factors for the considered load cycles is negligible for ships that mostly operate at steady load. Moreover, the load transitions during which CH<sub>4</sub> concentration was measured were more aggressive than what is experienced routinely during sailings. The transient measurements indicated the need for CH<sub>4</sub> emission reduction control strategies, particularly for transients with load reductions.</div></div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".