Enabling large-scale enhanced hydrogen production in deep underground coal gasification in the context of a hydrogen economy
Bibliographic record
Abstract
• A large-scale deep coal seam UCG model with real-world properties was constructed; • The mechanisms for large-scale enhanced hydrogen production were comprehensively investigated; • Water injection schemes were analyzed for optimal hydrogen production; • Proposed a novel technique for prolonged continuous hydrogen yield; • Revealed the advantages of the new technique in cavity control with environmental merits; Underground coal gasification (UCG) is an emerging clean energy technology with significant potential for enhanced hydrogen production, especially when coupled with water injection. Previous lab-scale studies have explored this potential, but the mechanisms driving water-assisted hydrogen enhancement in large-scale, deep UCG settings remain unclear. This study addresses this gap using numerical simulations of a large-scale deep coal model designed for hydrogen-oriented UCG. We investigated single-point and multipoint water injection strategies to optimize hydrogen production. Additionally, we developed a retractable water injection technique to ensure sustained hydrogen output and effective cavity control. Our results indicate that the water–gas shift reaction is crucial for increasing hydrogen production. Multipoint injection has been proven to be more effective than single-point injection, increasing hydrogen production by 11% with an equal amount of steam. The introduction of retractable injection allows for continuous and efficient hydrogen generation, with daily hydrogen production rates of approximately five times that of a conventional injection scheme, and an increase in cumulative hydrogen production of approximately 105% over the same time period. Importantly, the multipoint injection method also helped limit vertical cavity growth, mitigating the risk of aquifer contamination. These findings support the potential of UCG as a low-carbon energy source in the transition to a hydrogen economy.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".