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Record W4414164750 · doi:10.1002/ep.70100

Comparative techno‐economic and environmental study of hybrid and pure adsorption for carbon capture in blue hydrogen

2025· article· en· W4414164750 on OpenAlexafffund
Mohammad Sajjadi, Michael Fabrik, Hussameldin Ibrahim

Bibliographic record

VenueEnvironmental Progress & Sustainable Energy · 2025
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaMitacsPetroleum Technology Research CentreCanada Foundation for Innovation
KeywordsProfitability indexAdsorptionZeolitePayback periodLife-cycle assessmentCarbon fibersHydrogenCapital costHydrogen production

Abstract

fetched live from OpenAlex

Abstract Blue hydrogen offers a promising solution for mitigating global warming. Accordingly, improving carbon capture in blue H 2 plant designs becomes crucial. This study assesses and compares the viability of technologies for 100 and 400 tonnes‐per‐day plants, considering technical, environmental, and economic aspects using Aspen HYSYS and ReCiPe methodology for process simulation and life cycle assessment respectively. The hybrid absorption/adsorption system demonstrated superior long‐term profitability and higher net present value, despite its higher capital costs. In contrast, the adsorption‐only system resulted in a shorter payback period and lower equivalent emissions. The cost of hydrogen production was $2.20/kg for the former and $1.80/kg for the latter, with a slight difference in rate of return. Additionally, sensitivity analysis revealed the impact of natural gas costs, H 2 and CO 2 selling prices, and zeolite costs on profitability. Also, scale‐up showed that a plant with a larger capacity is more profitable.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.191
Teacher spread0.188 · 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 designSimulation or modeling
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

Citations1
Published2025
Admission routes2
Has abstractyes

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