Cold flow improvement of biodiesel and investigation of the effect of biodiesel emulsification on diesel engine performance and emissions
Bibliographic record
Abstract
Increasing concerns over environmental issues and conventional resource depletion have heightened our motivation to use clean and alternative fuels. Biodiesel is simply derived from biomass proposed as an alternative fuel for diesel engines, which contributes to a reduction in carbon monoxide (CO), smoke intensity, and unburned hydrocarbon (HC). However, biodiesel has inferior cold flow properties and emits higher nitrogen oxides (NOx) compared to conventional diesel. The present work aims at improving cold flow properties of biodiesel using the fractionation method combined with additives, and investigates their effects on a diesel engine?s regulated emissions and performance. In addition, emulsion fuels were found to reduce both NOx emission and smoke intensity. Experiments using urea, mixture of recovered urea and crystal, and crystal fractionation were conducted; the additives include ethanol, methanol, and diethyl ether (DEE). Results using two modern diesel engines (a light-duty and a heavy-duty) were investigated using various fuels. The heavy-duty engine was fueled with different fuel types and eight emulsion fuels at two idling conditions (1200 rpm and 1500 rpm). The light-duty engine was fueled with biodiesel blends, fractionated biodiesel blends, emulsified diesel-biodiesel, emulsified diesel-biodiesel ammonium hydroxides blends, and emulsified biodiesel at three different engine operating conditions. The conclusion was that a mixture of recovered urea and crystal fractionation provided higher production efficiency and acceptable cloud point. A significant reduction in NOx emission was obtained from emulsified fuels compared with their bases, and emulsion biodiesel with 2.5% water revealed results that were comparable to diesel in terms of NOx and CO emissions at all engine operating conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".