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Enabling large-scale enhanced hydrogen production in deep underground coal gasification in the context of a hydrogen economy

2024· article· en· W4405839020 on OpenAlexaff
Zixiang Wei, Liangliang Jiang, Aliakbar Hassanpouryouzband, Shanshan Chen, Yanpeng Chen, Yiwen Ju, Lele Feng, Kouqi Liu, Jiansheng Zhang, Zhangxin Chen, S.M. Farouq Ali

Bibliographic record

VenueEnergy Conversion and Management · 2024
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHydrogen productionHydrogen economyContext (archaeology)Coal gasificationCoalHydrogenWaste managementScale (ratio)Production (economics)Underground coal gasificationEnvironmental scienceProcess engineeringEngineeringChemistryEconomicsGeologyPhysics

Abstract

fetched live from OpenAlex

• 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.203
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2024
Admission routes1
Has abstractyes

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