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Comparative assessment of blue hydrogen from steam methane reforming, autothermal reforming, and natural gas decomposition technologies for natural gas-producing regions

2022· article· en· W4207064172 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Conversion and Management · 2022
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHydrogen productionGreenhouse gasNatural gasSteam reformingEnvironmental scienceMethaneLife-cycle assessmentCarbon footprintCarbon sequestrationWaste managementMethane reformerHydrogenProduction (economics)EngineeringChemistryCarbon dioxide

Abstract

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Interest in blue hydrogen production technologies is growing. Some researchers have evaluated the environmental and/or economic feasibility of producing blue hydrogen, but a holistic assessment is still needed. Many aspects of hydrogen production have not been investigated. There is very limited information in the literature on the impact of plant size on production and the extent of carbon capture on the cost and life cycle greenhouse gas (GHG) emissions of blue hydrogen production through various production pathways. Detailed uncertainty and sensitivity analyses have not been included in most of the earlier studies. This study conducts a holistic comparative cost and life cycle GHG emissions’ footprint assessment of three natural gas-based blue hydrogen production technologies – steam methane reforming (SMR), autothermal reforming (ATR), and natural gas decomposition (NGD) to address these research gaps. A hydrogen production plant capacity of 607 tonnes per day was considered. For SMR, based on the percentage of carbon capture and capture points, we considered two scenarios, SMR-52% (indicates 52% carbon capture) and SMR-85% (indicates 85% carbon capture). A scale factor was developed for each technology to understand the hydrogen production cost with a change in production plant size. Hydrogen cost is 1.22, 1.23, 2.12, 1.69, 2.36, 1.66, and 2.55 $/kg H2 for SMR, ATR, NGD, SMR-52%, SMR-85%, ATR with carbon capture and sequestration (ATR-CCS), and NGD with carbon capture and sequestration (NGD-CCS), respectively. The results indicate that when uncertainty is considered, SMR-52% and ATR are economically preferable to NGD and SMR-85%. SMR-52% could outperform ATR-CCS when the natural gas price decreases and the rate of return increases. SMR-85% is the least attractive pathway; however, it could outperform NGD economically when CO2 transportation cost and natural gas price decrease. Hydrogen storage cost significantly impacts the hydrogen production cost. SMR-52%, SMR-85%, ATR-CCS, and NGD-CCS have scale factors of 0.67, 0.68, 0.54, and 0.65, respectively. The hydrogen cost variation with capacity shows that operating SMR-52% and ATR-CCS above hydrogen capacity of 200 tonnes/day is economically attractive. Blue hydrogen from autothermal reforming has the lowest life cycle GHG emissions of 3.91 kgCO2eq/kg H2, followed by blue hydrogen from NGD (4.54 kgCO2eq/kg H2), SMR-85% (6.66 kgCO2eq/kg H2), and SMR-52% (8.20 kgCO2eq/kg H2). The findings of this study are useful for decision-making at various levels.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

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 science0.0000.001
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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