Assessment of Renewable Natural Gas Refueling Stations for Heavy-Duty Vehicles
Bibliographic record
Abstract
Abstract With environmental concerns and limited natural resources, there is a need for cleaner sources of energy in the transportation sector. Renewable natural gas (RNG) is being considered as a potential fuel for heavy-duty applications due to its comparable usage to diesel and gasoline in vehicles. The idea of compressed RNG vehicles is being proposed especially because it will significantly reduce harmful emissions into the environment. This study examines the feasibility of implementing a nationwide network of compressed RNG refueling infrastructure in order to accommodate a conversion of long-haul, heavy-duty (LHHD) truck fleet from diesel fuel to RNG. Two methods, Constant Traffic and Variable Traffic, along with data about compressed RNG infrastructure and vehicles, were developed and used to predict fuelling requirements for LHHD truck fleet. Then, a detailed economic analysis was conducted on various test cases to estimate how different variables impact the final selling price of RNG. This provided insight with the understanding of what factors go into pricing RNG and if it can compete against diesel in the trucking market. Results disclosed that the cost to purchase RNG is the greatest factor in the final selling price of compressed RNG. Due to the variability in RNG production, however, there is no precise cost, which makes predictions challenging. However, results revealed that it is possible for compressed RNG to be competitive with diesel, with the mean compressed RNG price being 16.5% cheaper than diesel, before being taxed. Future studies should focus on the feasibility of the production of RNG and the associated costs. An in-depth analysis on operational and maintenance costs for compressed RNG refueling stations may also provide predictions that are more accurate. The methodology developed in this feasibility analysis may serve as a useful tool for future techno-economics of RNG refueling stations for other types of internal combustion engine (ICE) fleets or those powered with alternative green fuels.
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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 | 0.000 |
| 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) | 0.000 | 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".