Upgrading of Waste Heat for Combined Power and Hydrogen Production With Nuclear Reactors
Bibliographic record
Abstract
This paper presents a new heat upgrading method that utilizes waste heat from nuclear reactors for thermochemical water splitting with a copper-chlorine (Cu–Cl) cycle. Through combined power, hydrogen, and oxygen generation, the exergy efficiency of a power plant can be significantly augmented. The heat rejected to the environment for moderator cooling, a relatively small amount of low pressure superheated steam and a small fraction of generated power, are extracted from the nuclear reactor and used to drive a Cu–Cl hydrogen plant. More specifically, the moderator heat transfer at ∼80°C is used as a source to a newly proposed vapor compression heat pump with a cascaded cycle, operating with retrograde fluids of cyclohexane (bottoming cycle) and biphenyl (topping supercritical cycle). Additionally, the heat pump uses as input the heat recovered from within the Cu–Cl cycle itself. This heat is recovered at two levels: ∼80–130°C and ∼250–485°C. This heat input is upgraded up to 600°C by work-to-heat conversion and then used to supply the endothermic water splitting process. The extracted steam is fed into the Cu–Cl cycle and split into hydrogen and oxygen as overall products. Electricity is partly used for an electrochemical process within the Cu–Cl cycle, and also partly for the heat pump compressors. This paper analyses the performance of the proposed heat pump and reports the exergy efficiency of the overall system. The proposed system is about 4% more efficient than generating electricity alone from the nuclear reactor.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".