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Record W7081942201 · doi:10.11159/iccpe25.103

Development of Hybrid Blue Ammonia Process for Co-production of Green and Blue Ammonia

2025· article· en· W7081942201 on OpenAlexvenueno aff

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)AmmoniaAmmonia productionSulfuric acid

Abstract

fetched live from OpenAlex

Blue Ammonia, a low-carbon variant of ammonia, is produced using natural gas feedstock while incorporating carbon capture and storage (CCS) to minimize CO₂ emissions.It is a crucial energy carrier and Hydrogen transport medium for long-distance supply chains, playing a vital role in the global transition toward carbon neutrality.This study simulates a blue Ammonia production process utilizing methane as the primary feedstock, integrated with a maximum-efficiency CO₂ capture unit and the ability to produce green Ammonia by attaching a synloop in parallel.The proposed system operates in a hybrid mode, characterized by a flexible design that incorporates both green (renewable-based) and blue (fossil-based with carbon capture and storage, or CCS) feedstocks.Simulation results indicate that a hybrid plant with a 1,660 metric tons per day (MTPD) natural gas input achieves a fuel/feedstock ratio of 49.1%, demonstrating feasibility for large-scale production.Additionally, captured CO₂ is compressed to 60 bar, facilitating export for enhanced oil recovery (EOR) or pipeline transport.The study confirms that this blue Ammonia process can achieve an overall CO₂ capture efficiency of up to 98.5%.Given its high CO₂ reduction potential, blue Ammonia significantly contributes to global decarbonization efforts and Germany's Energiewende by providing a low-carbon Hydrogen carrier.It enhances energy security, supports industrial decarbonization, and bridges the gap between fossil-based and renewable Hydrogen, making it a key component of a sustainable energy transition worldwide.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models agreeAgreement compares identical category sets and study designs across arms.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2025
Admission routes1
Has abstractno

Explore more

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