Experimental Determination of the Efficiency and Emissions of a Residential Microcogeneration System Based on a Stirling Engine and Fueled by Diesel and Ethanol
Bibliographic record
Abstract
Renewable forms of energy, such as biofuels, have the potential to displace fossil fuels in a wide variety of applications. Further benefit can be achieved through the use of combined heat and power devices, as this is accompanied with a considerable increase in energy efficiency and lower costs associated with fuel consumption. In this study, we examined the performance of a residential microcogeneration system based on a Stirling engine and fueled by diesel and ethanol. Run on diesel, and on a lower heating value basis, the system achieved a power efficiency of 12.1%, a thermal efficiency of 73.3%, and a total efficiency of 85.4%. Powered by ethanol, the corresponding efficiencies were 11.8%, 73.9%, and 85.7%, respectively. During steady state operation, the total unburned hydrocarbon emissions for both fuels were negligible, while the particulate emissions for ethanol and diesel were found to be 0.40 mg/kWh and 0.42 mg/kWh, respectively. Emissions were extremely low, as the combustor features a continuous premixed flame that facilitates the complete burnout of already evaporated fuel. Though emissions of nitrogen dioxide, methane, formaldehyde, and acetaldehyde were also negligible for both fuels, carbon monoxide and nitric oxide emissions for diesel (71 and 67 mg/m 3, respectively) were much higher than those observed for ethanol (50 and 19 mg/m 3, respectively). Lower nitric oxide emission levels for ethanol were attributed to its lower flame temperature, whereas reductions in carbon monoxide emissions were likely a result of a higher degree of fuel/air mixing with ethanol, due to higher gas jet velocities of the fuel exiting the orifices of the evaporator. Lastly, parametric studies on primary engine set points, including coolant temperature and exhaust temperature, were conducted to understand their effect on engine performance.
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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".