Modeling of Off-Gas Emissions from Wood Pellets During Marine Transportation
Bibliographic record
Abstract
After a fatal accident during the discharge of wood pellets at Helsingborg, emissions from pellets during marine transportation became a concern for the safe handling and storage of wood pellets. In this paper, a two-compartment model has been developed for the first time to predict the concentrations of CO, CO₂, CH₄, and O₂ inside the cargo ship and the time and rate of forced ventilation required before the safe entry into the stairway adjacent to the storage hatch. The hatch and stairway are treated as two perfectly mixed tanks. The gas exchange rate between these two rooms and the gas exchange rate with the atmosphere are fitted to satisfy a measured tracer final concentration of 33 p.p.m.v. in the stairway and an average final hatch to stairway CO, CO₂, and CH₄ concentration ratio of 1.62 based on measurement from five other hatch and stairway systems. The reaction kinetics obtained from a laboratory unit using a different batch of pellets, however, need to be scaled in order to bring the prediction to close agreement with onboard measured emission data at the end of voyage. Using the adjusted kinetic data, the model was able to predict the general trend of data recorded in the first 12.5 days of the voyage. Further validation, however, requires the data recorded over the whole journey. The model was applied to predict the effect of ocean temperature on the off-gas emissions and the buildup of concentrations in the hatch and stairway. For safe entry to the cargo ship, the current model predicted that a minimal ventilation rate of 4.4 hr⁻¹ is required for the stairway's CO concentration to lower to a safe concentration of 25 p.p.m.v. At 4.4 hr⁻¹, 10 min of ventilation time is required for the safe entry into the stairway studied.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".