Replacement of Diesel by Biogas Generated From Wastewater Treatment in a Small Diesel Generator by Dual Fuel Technology
Bibliographic record
Abstract
Abstract Canada’s remote communities experience harsh weather much of the year and run diesel generators 24 hours a day to provide heat and power. These generators utilize diesel fuel that is transported at great expense and generate greenhouse gas (GHG) and pollutant emissions. Meanwhile, remote communities produce organic wastes, such as wastewater and food wastes. Appropriate treatment of these wastes not only improves the community health and environment, but also generates certain amount of renewable fuels, such as biogas and/or syngas. Replacing diesel fuel by the renewable fuels generated from the waste treatment in the diesel generators can offset the use of diesel and also reduce GHG and pollutant emissions in remote communities. This paper reports an application of biogas generated from wastewater treatment to replace diesel in a small diesel generator by dual fuel engine technology. With simple modification, the biogas containing 50–95% of methane was introduced into the engine intake manifold. Tests were conducted to evaluate the effects of biogas flow rate and composition on average diesel fuel consumption and emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx) and unburned hydrocarbons (HC). The results reveal that the introduction of biogas into the engine reduced the average diesel consumption. However, the reduction of average diesel consumption with increasing biogas flow rate was not linear, possibly due to the increase in HC emissions. The introduction of biogas reduced NOx emissions but increased CO emissions. A change in the composition of biogas (methane to CO2 ratio) did not significantly affect the average diesel consumption and 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 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 | 0.001 | 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".