Water intensity for hydrogen production with and without carbon capture and sequestration
Bibliographic record
Abstract
Low-carbon hydrogen has gained significant attention, particularly in nations with abundant fossil fuel reserves. While the techno-economic and greenhouse gas (GHG) emission performance of hydrogen production with carbon capture and storage (CCS) is well understood, its impact on water demand remains underexplored. This study evaluates water demand, including water withdrawal, consumption, and discharge, in hydrogen production via steam methane reforming (SMR), auto-thermal reforming (ATR), and natural gas decomposition (NGD), both with and without CCS. CO 2 is captured using commercially available amine scrubbing technology and transported via CO 2 pipelines. Three SMR-CCS configurations were analyzed: SMR-52% (52% CO 2 capture from syngas), SMR-85% (85% CO 2 capture from syngas and flue gas in separate units), and SMR-86% (86% CO 2 capture from syngas and flue gas in one integrated unit). Water withdrawal values (L/kg-H 2 ) for SMR, ATR, catalytic-thermal NGD, plasma NGD, SMR-52%, SMR-85%, SMR-86%, ATR-CCS, and catalytic-thermal NGD-CCS are 10.29, 14.27, 5.37, 22.57, 12.10, 18.41, 23.36, 16.98, and 7.32, respectively. Water consumption values are 5.34, 9.33, 4.30, 16.73, 6.91, 11.01, 14.43, 11.69, and 5.68 L/kg-H 2 , respectively. Hydrogen production efficiency was evaluated through net energy ratios (NERs), which range from 0.43 to 0.76. The findings indicate that CCS increases water demand while reducing overall hydrogen production efficiency. These results provide valuable insights for policymakers and industry stakeholders, helping identify challenges and opportunities for sustainable hydrogen deployment, particularly in water-scarce regions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".