Comparative assessment of blue hydrogen from steam methane reforming, autothermal reforming, and natural gas decomposition technologies for natural gas-producing regions
Bibliographic record
Abstract
Interest in blue hydrogen production technologies is growing. Some researchers have evaluated the environmental and/or economic feasibility of producing blue hydrogen, but a holistic assessment is still needed. Many aspects of hydrogen production have not been investigated. There is very limited information in the literature on the impact of plant size on production and the extent of carbon capture on the cost and life cycle greenhouse gas (GHG) emissions of blue hydrogen production through various production pathways. Detailed uncertainty and sensitivity analyses have not been included in most of the earlier studies. This study conducts a holistic comparative cost and life cycle GHG emissions’ footprint assessment of three natural gas-based blue hydrogen production technologies – steam methane reforming (SMR), autothermal reforming (ATR), and natural gas decomposition (NGD) to address these research gaps. A hydrogen production plant capacity of 607 tonnes per day was considered. For SMR, based on the percentage of carbon capture and capture points, we considered two scenarios, SMR-52% (indicates 52% carbon capture) and SMR-85% (indicates 85% carbon capture). A scale factor was developed for each technology to understand the hydrogen production cost with a change in production plant size. Hydrogen cost is 1.22, 1.23, 2.12, 1.69, 2.36, 1.66, and 2.55 $/kg H2 for SMR, ATR, NGD, SMR-52%, SMR-85%, ATR with carbon capture and sequestration (ATR-CCS), and NGD with carbon capture and sequestration (NGD-CCS), respectively. The results indicate that when uncertainty is considered, SMR-52% and ATR are economically preferable to NGD and SMR-85%. SMR-52% could outperform ATR-CCS when the natural gas price decreases and the rate of return increases. SMR-85% is the least attractive pathway; however, it could outperform NGD economically when CO2 transportation cost and natural gas price decrease. Hydrogen storage cost significantly impacts the hydrogen production cost. SMR-52%, SMR-85%, ATR-CCS, and NGD-CCS have scale factors of 0.67, 0.68, 0.54, and 0.65, respectively. The hydrogen cost variation with capacity shows that operating SMR-52% and ATR-CCS above hydrogen capacity of 200 tonnes/day is economically attractive. Blue hydrogen from autothermal reforming has the lowest life cycle GHG emissions of 3.91 kgCO2eq/kg H2, followed by blue hydrogen from NGD (4.54 kgCO2eq/kg H2), SMR-85% (6.66 kgCO2eq/kg H2), and SMR-52% (8.20 kgCO2eq/kg H2). The findings of this study are useful for decision-making at various levels.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".