Experimental Underperformance Detection of a Fixed-Speed Diesel–Electric Generator Based on Exhaust Gas Emissions
Bibliographic record
Abstract
Low load is one of the most challenging combustion stages for a fixed-speed diesel electric generator. Due to incomplete combustion during this phase, a significant proportion of contaminants form inside the cylinder. This can lead to numerous chemical and mechanical harms to the diesel engine, resulting in friction, efficiency reduction, increased fuel consumption, and prematurely ending the generator’s life. These phenomena are qualified as underperformance, possibly due to a misfire and/or a low-efficiency value (air fuel–fuel ratio). Therefore, detecting and preventing underperformance and reducing its extended operation is crucial. This paper deals with the performance and emission analysis of a multicylinder fixed-speed diesel engine driving an electric generator (300 kW) fueled with ultra-low sulfur diesel (≤15 mg/kg) to provide energy in an isolated Canadian community. The tests were carried out according to ISO 3046-1:2002 standard in a remote site to identify clues that can prevent prolonged operation in underperformance. Among the tests conducted, emissions such as sulfur (S), carbon dioxide (CO2), nitrogen oxide (NOx), and exhaust gas temperature are considered the best indices for detecting the underperformance of a fixed-speed diesel–electric generator under very-low and low load (0–30%) with the following registered values: 18 ppm for S, 4% for CO2, 150 ppm for NOx, and 210 °C for the temperature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".