Efficiency, economic and environmental impact assessments of a new integrated rail engine system using hydrogen and other sustainable fuel blends
Bibliographic record
Abstract
Locomotives still use antiquated engines, such as internal combustion engines operated by fossil fuels which cause global warming due to their significant emissions. This paper investigates a new hybridized locomotive engine containing a gas turbine system, solid oxide fuel cell system, heat recovery system, and an on-board hydrogen production system. This new integrated engine is operated using five fuel blends composed of alternative fuels, such as hydrogen, methane, methanol, ethanol and dimethyl ether. The current investigation involves multiple studies, such as exergy analysis, exergoeconomic analysis and exergoenvironmental analysis to assess the integrated engine system from three perspectives: efficiency/irreversibility, cost and environmental impact. The present study results show that the net power of this new engine is 4948.6 kW, and it has an exergetic efficiency of 62.7% according to the fuel-product principle. In addition, this engine weighs about 9 tons and costs about $10.2 M, with a levelized cost rate of 147 $/h and 14.06 mPt/h of overall component-related environmental impact rate. The average overall specific fuel and product exergy costs are about 37 $/GJ and 60 $/GJ, and the minimum values are 13.3 $/GJ and 21.8 $/GJ using the methane and hydrogen blend, respectively. Also, the average overall specific fuel-product exergoenvironmental impacts are about 15 and 23 mPt/MJ, respectively. Furthermore, the on-board hydrogen production has an average exergy cost of 274 $/GJ with an environmental impact of 52 mPt/MJ. Moreover, the hydrogen blended with methane or methanol is found to be more economical with less environmental impact.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".