Effect of a New Design Electronic Control System on the Emissions Improve for Diesel Engine Operation by (Diesel + LPG)
Bibliographic record
Abstract
Diesel engines are important and widely used in many fields in industry, agriculture, transportation, and electricity, but the disadvantages of these engines are environmental pollution due to exhaust gas emissions as well as the high cost of diesel fuel.These defects made the topic an important research topic to search for less polluting and less expensive fuel to use it in diesel engines, and this makes LPG a good candidate for diesel supplements because it contains several technical advantages in this regard, being environment friendly and has a high heat value to increase energy production And also its price is cheap compared to diesel fuel.In this study, an electronic system was designed to control the LPG injector and a magnetic sensor was installed on top of a single-cylinder and air-cooled diesel engine head.Tested using at two-stage first diesel fuel D-100 and second stage, dual-fuel in three modes .The test was under loads (0%, 25%, 50%, 75% and 100%) at different speeds (1000, 1500 and 2000 rpm).A decrease in emission ratios (NOx, HC, CO, and CO2) is observed in all operating modes with LPG, and the best emission reduction mode is LPG-75.As for O2 gas, the results showed almost the same in diesel case.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".