Hydrogen Generation Can Reduce New Plant Design and Building Costs
Bibliographic record
Abstract
Large electric power plant generators typically use gaseous hydrogen to remove heat from the generator windings and deliver the heat to the cooling water. Hydrogen is used in a closed cycle, and only a modest amount of makeup hydrogen is used daily to make up for hydrogen losses — typically about 300 to 700 scf/d. The range of hydrogen usage depends on several factors. In addition to hydrogen used for makeup, all power plants using hydrogen-cooled generators must plan for hydrogen supply to re-gas a generator after the generator has been degassed. Typical generator re-gas quantities are in the range of 15× the daily makeup amount, and must be available in a short period of time. Thus a generator which might require 300 to 700 scf of hydrogen over 24 hours for daily makeup may require 4500 to 10,500 scf of hydrogen in just a few hours for re-gas. The re-gas hydrogen is added back to the generator as quickly as the re-gas process allows — typically over 3–5 hours — so that an out-of-service generator can be brought online and producing revenue again. Hydrogen for power plant generator cooling can be supplied either through hydrogen delivered to the plant from a remote source in gaseous or liquid form, or can be made at the plant using an on-site hydrogen generator. Makeup hydrogen and re-gas hydrogen do not necessarily require the same source of hydrogen — because the requirements of re-gas hydrogen are very different from the requirements of makeup hydrogen, it may be more efficient to use two different approaches. On-site hydrogen generation for power plant hydrogen supply is widespread in the developing world, and is beginning to displace delivered hydrogen as the preferred approach in the U.S., Canada and Europe. Outside U.S., Canada and Europe, there may be no delivery infrastructure for hydrogen manufacture and delivery to the plant — a hydrogen-cooled power plant may need to take care of its own hydrogen needs to ensure that the plant can be operated. In the U.S., Canada and Europe hydrogen deliveries are available, but on-site generated hydrogen is gaining acceptance because it reduces costs and operational complexity, and improves safety. This paper will review several cases where on-site hydrogen generation has been used to reduce the cost of design, construction and operation of newly built power plants, both in the U.S., Canada and Europe and in areas where hydrogen is far less available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.106 | 0.037 |
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".