Highway Exhaust Emissions of a Natural Gas-Diesel Dual-Fuel Heavy-Duty Truck
Bibliographic record
Abstract
Diesel-fueled heavy-duty vehicles (HDVs) can be retrofitted with conversion kits to operate as dual-fuel vehicles in which partial diesel usage is offset by a gaseous fuel such as compressed natural gas (CNG). The main purpose of installing such a conversion kit is to reduce the operating cost of HDVs. Additionally, replacing diesel partially with a low-carbon fuel such as CNG can potentially lead to lower carbon dioxide (CO2) emissions in the tail-pipe. The main issue of CNG-diesel dual-fuel vehicles is the methane (CH4, the primary component of CNG) slip. CH4 is difficult to oxidize in the exhaust after-treatment (EAT) system and its slip may offset the advantage of lower CO2 emissions of natural gas combustion as CH4 is a strong greenhouse gas (GHG). The objective of this study is to compare the emissions of an HDV with a CNG conversion kit operating in diesel and dual-fuel mode during highway operation. Road tests were conducted on a three-axle Class-8 highway semi-trailer tractor hauling a two-axle loaded box trailer. The gross combined weight of the tractor-trailer was 34,470 kg (~76,000 lbs). The tractor was powered by an inline 6-cylinder, direct injection diesel engine with EAT system, and met EPA 2010 emission regulations. The primary components of the conversion kit were: CNG tank, regulator, and mixing manifold with solenoid CNG injectors. CNG was injected into the intake manifold of the engine downstream of the intercooler. The CNG injection map was based on the throttle position, engine speed, load, and intake boost pressure. Portable emissions measurement systems (PEMS) were used to analyze the exhaust gas before and after the EAT system. The vehicle’s onboard diagnostic (OBD) data was also recorded concurrently. The highway test route was 74 km long and the average road speed was ~102 km/h. Results showed that up to 34% of the diesel consumption could be replaced by CNG. When compared to diesel-only, the CO2 and total hydrocarbon emissions of the dual-fuel case were lower and higher, respectively. Engine-out black carbon emissions were lower for the dual-fuel case in comparison to diesel, while tail-pipe nitrogen oxides (NOx) emissions were higher. Distinct differences in the exhaust temperature profiles were observed as well.
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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) | 0.002 | 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".