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Record W4411536037 · doi:10.3390/en18133272

Case Study of a Greenfield Blue Hydrogen Plant: A Comparative Analysis of Production Methods

2025· article· en· W4411536037 on OpenAlexafffundabout
Mohammad Sajjadi, Hussameldin Ibrahim

Bibliographic record

VenueEnergies · 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 InnovationUniversity of Regina
KeywordsGreenhouse gasCarbon footprintEnvironmental scienceHydrogen productionSustainabilityGlobal warmingNatural gasProduction (economics)Waste managementEnvironmental engineeringRefrigerationEnvironmental economicsEngineeringClimate changeHydrogenChemistryEconomics

Abstract

fetched live from OpenAlex

Blue hydrogen is a key pathway for reducing greenhouse gas emissions while utilizing natural gas with carbon capture and storage (CCS). This study conducts a techno-economic and environmental analysis of a greenfield blue hydrogen plant in Saskatchewan, Canada, integrating both SMR and ATR technologies. Unlike previous studies that focus mainly on production units, this research includes all process and utility systems such as H2 and CO2 compression, air separation, refrigeration, co-generation, and gas dehydration. Aspen HYSYS simulations revealed ATR’s energy demand is 10% lower than that of SMR. The hydrogen production cost was USD 3.28/kg for ATR and USD 3.33/kg for SMR, while a separate study estimated a USD 2.2/kg cost for design without utilities, highlighting the impact of indirect costs. Environmental analysis showed ATR’s lower Global Warming Potential (GWP) compared to SMR, reducing its carbon footprint. The results signified the role of utility integration, site conditions, and process selection in optimizing energy efficiency, costs, and sustainability.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.915
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.320
Teacher spread0.288 · 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 teacher head, 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

Citations6
Published2025
Admission routes3
Has abstractyes

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