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Record W2029938701 · doi:10.1115/icef2005-1235

Diesel Engine NOx Reduction Using Charge Air Water Injection

2005· article· en· W2029938701 on OpenAlex
Ernst Radloff, Charles Antoine Gautier

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsTransport Canada
Fundersnot available
KeywordsNOxDiesel fuelEnvironmental scienceParticulatesEngineeringWaste managementNitrogen oxideEnvironmental engineeringChemistry

Abstract

fetched live from OpenAlex

The Transportation Development Centre of Transport Canada, in collaboration with Environment Canada’s Emissions Research and Measurement Division, conducted a series of emissions tests onboard the Oceanex RoRo vessel MV Cabot operating between Montre´al, Quebec, and St. John’s, Newfoundland. The primary objectives were to verify emissions inventories and demonstrate the feasibility of installing affordable emissions reduction technology on marine vessels as well as compliance with future regulatory emissions limits. The tests also provided an opportunity for Canada to share information on emissions program and technology developments with U.S. regulatory authorities. This may lead to developing joint emissions reduction initiatives for existing marine vessels. This paper describes the field-testing of a water injection system (WIS) to reduce oxides of nitrogen (NOx) emissions from ocean-going vessels. Tests were conducted on a semi-dedicated basis during voyage and under steady-state conditions. The emissions measurements were taken in accordance with ISO 8178-4-E3 protocol and using both marine diesel oil and intermediate fuel oil, which enabled the evaluation of the impact of different fuel type and quality on emissions. An initial series of tests was carried out on the MV Cabot in March 2004, followed by a second series of tests on the same vessel in March 2005. These tests demonstrated the effectiveness of a low-cost WIS for reducing NOx emissions in marine diesel engines. They also showed that water injection reduces NOx at the expense of an increase in both particulate matter and carbon monoxide when using intermediate fuel oil. NOx reductions varied between 10 and 35 percent, and were most effective at high water injection ratios above 50 percent engine load. The test results showed no negative impact of the WIS on fuel consumption or engine operation and performance. This paper compares the results obtained from the consecutive series of tests in terms of the effectiveness of NOx reduction, and analyses the results in the context of other full-scale test results obtained from emissions control system vendors and engine suppliers. It also investigates the theoretical process and technology of water injection through charge air fumigation, and both direct water and fuel/water emulsion injection. In addition, the effects of water injection on engine emissions, operation and maintenance, and the optimization of water injection from a knowledge-based perspective are discussed. Further testing and development of the WIS are required to realize optimal emissions reduction potential and to determine the impact of water injection on fuel consumption, and engine operational performance as well as the impact of fuel quality on emissions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0540.001

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.

Opus teacher head0.009
GPT teacher head0.210
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it