Water Splitting Technologies for Hydrogen Cogeneration from Nuclear Energy
Bibliographic record
Abstract
Currently, nuclear energy is mainly utilized for the generation of electricity that is distributed to end users via power transmission networks. However, there are also other distribution forms. For example, hydrogen produced from nuclear energy is a promising future energy carrier that can be delivered to end users for purposes of heating homes, fuel supply for hydrogen vehicles and other residential applications, while simultaneously lowering the greenhouse gas emissions of otherwise using fossil fuels Current industrial demand for hydrogen exists in the upgrading of heavy oils such as oil sands, refineries, fertilizers, automotive fuels, and manufacturing applications among others. Hydrogen production is currently a large, rapidly growing and profitable industry. The worldwide hydrogen market is currently estimated at about $300 billion per year, growing at about 10% per year, growing to 40% per year by 2020 and expected to reach several trillions of dollars per year by 2020 This chapter will examine the usage of nuclear energy for the cogeneration of electricity and hydrogen with water splitting technologies. In section 2 of this chapter, various hydrogen production methods will be briefly introduced and compared. The potential economics and reduction of greenhouse gas emissions with nuclear hydrogen production are examined. In section 3, matching the heat requirements of various thermochemical hydrogen cycles to the available heat from nuclear reactors (especially Generation IV) will be studied from the aspects of heat grade, magnitude, and distribution inside the cycles. The requirement of an intermediate heat exchanger between the nuclear reactor and hydrogen production plant is discussed. Long distance heat transport is examined from the aspects of the performance of working fluids, flow characteristics, and heat losses in the transport pipeline. In section 4, layout options for the integration of nuclear reactors and hydrogen production plants are discussed. In section 5, modulations of nuclear energy output and hydrogen cogeneration scales are studied, regarding the increase of the nuclear energy portion on the power grid through the adjustment of the hydrogen production rate so as to lower the needs for fossil fuels. The options for keeping the total nuclear energy output at a constant value and simultaneously varying the electricity output onto the power grid in order to approach a load following profile for peak and off-peak hours are discussed. In section 6, conclusions are provided for the cogeneration of hydrogen with nuclear heat.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".