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Record W4406186234 · doi:10.1016/j.energy.2025.134398

Hydrogen production in integration with CCUS: A realistic strategy towards net zero

2025· article· en· W4406186234 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 · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Calgary
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsZero (linguistics)Production (economics)Zero emissionHydrogen productionEnvironmental scienceNet (polyhedron)HydrogenEngineeringWaste managementEconomicsPhysicsMathematicsMicroeconomics

Abstract

fetched live from OpenAlex

It is believed that hydrogen will play an essential role in energy transition and achieving the net-zero target by 2050. Currently, global hydrogen production mostly relies on processing fossil fuels such as coal and natural gas, commonly referred to as grey hydrogen production while releasing substantial amounts of carbon dioxide (CO 2 ). Developing economically and technologically viable pathways for hydrogen production while eliminating CO 2 emissions becomes paramount. In this critical review, we examine the common grey hydrogen production techniques by analyzing their technical characteristics, production efficiency and costs. We further analyze the integration of carbon capture, utilization and storage (CCUS) technology, establishing the zero-carbon strategy transiting from grey to blue hydrogen production with CO 2 capture and either utilized or permanently stored. Today, grey hydrogen production exhibits technological diversities, with various commercial maturities. Most methods rely on the effectiveness of catalysts, necessitating a solution to address catalyst fouling and sintering in practice. Although CCUS captures, utilizes or stores CO 2 during grey hydrogen production, its wide application faces multiple challenges regarding the technological complexity, cost, and environmental benefits. It is urgent to develop technologically mature, low-cost and low-energy-consumption CCUS technology, implementing extensive, large-scale integrated pilot projects. • Analyze the value chain of hydrogen production integrated with carbon capture, utilization and storage • Discuss novel techniques on the development of catalyst performance for hydrogen production • Analyze different carbon capture methods specifically for hydrogen production contexts • Conduct techno-economic analysis of hydrogen production integrated with carbon capture, utilization and storage

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 score0.401
Threshold uncertainty score0.586

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 science0.0000.000
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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