Emissions from a Diesel Engine using Fe-based Fuel Additives and a Sintered Metal Filtration System
Bibliographic record
Abstract
A series of laboratory tests were conducted to assess the effects of Fe-containing fuel additives on aerosols emitted by a diesel engine retrofitted with a sintered metal filter (SMF) system. Emission measurements performed upstream and downstream of the SMF system were compared, for cases when the engine was fueled with neat ultralow sulfur diesel (ULSD) and with ULSD treated with two formulations of additives containing Fe-based catalysts. The effects were assessed for four steady-state engine operating conditions and one transient cycle. The results showed that the SMF system reduced the average total number and surface area concentrations of aerosols by more than 100-fold. The total mass and elemental carbon results confirmed that the SMF system was indeed very effective in the removal of diesel aerosols. When added at the recommended concentrations (30 p.p.m. of iron), the tested additives had minor adverse impacts on the number, surface area, and mass concentrations of filter-out (FOut) aerosols. For one of the test cases, the additives may have contributed to measurable concentrations of engine-out (EOut) nucleation mode aerosols. The additives had only a minor impact on the concentration and size distribution of volatile and semi-volatile FOut aerosols. Metal analysis showed that the introduction of Fe with the additives substantially increased Fe concentration in the EOut, but the SMF system was effective in removal of Fe-containing aerosols. The FOut Fe concentrations for all three tested fuels were found to be much lower than the corresponding EOut Fe concentrations for the case of untreated ULSD fuel. The results support recommendations that these additives should not be used in diesel engines unless they are equipped with exhaust filtration systems. Since the tested SMF system was found to be very efficient in removing Fe introduced by the additives, the use of these additives should not result in a measurable increase in emissions of de novo generated Fe-containing aerosols. The findings from this study should promote a better understanding of the benefits and challenges of using sintered metal systems and fuel additives to control the exposure of underground miners and other workers to diesel aerosols and gases.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".