Hydrogen production in integration with CCUS: A realistic strategy towards net zero
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".