Techno-Economic Analysis of Hydrogen–Diesel Dual-Fuel Engines as a Transitional Decarbonization Strategy for Mining Haul Trucks
Bibliographic record
Abstract
This study evaluates the emissions, refueling or recharging times, and payload capacities of hydrogen–diesel dual-fuel, battery-electric, and hydrogen fuel cell haul trucks for open-pit mining operations. Both shift-level and long-term cumulative performances are assessed under realistic operational constraints. A generic model simulates optimal truck configurations over a 25 year period (2025–2050), assuming equivalent payload capacity per truck. Payload per shift depends on the number of haulage cycles, which varies with energy density, refueling or recharging times, and maintenance requirements of each technology. Under the simulated settings and imposed assumptions, results show that while battery-electric and hydrogen fuel cell trucks achieve zero tailpipe emissions, they incur substantial cumulative payload losses (65 and 25 Mt, respectively) relative to a diesel baseline. This is primarily due to longer refueling or recharging times and lower energy density. In contrast, by flexibly adjusting fuel shares to meet tightening emission limits, hydrogen–diesel dual-fuel trucks experienced limited impact with a cumulative payload loss of 15 Mt. These differences translate into effective cost of baseline payload estimates, with dual-fuel trucks rising from AU$1.00/t to AU$1.40/t by 2050, compared to AU$1.67/t for battery-electric trucks and AU$1.70/t for fuel cell trucks. A sensitivity analysis highlights the influence of mining road conditions, discount rates, fuel prices, and efficiency degradation over time. The findings highlight the potential of hydrogen–diesel dual-fuel trucks to provide a cost-effective transitional pathway for decarbonizing mining haulage under the simulated conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".