Multi-Purpose Hydrogen Isotopes Separation Plant Design
Bibliographic record
Abstract
There is a potential interest at CRL to detritiate moderately tritiated light water and to reclaim tritiated, downgraded heavy water. With only a few limitations, a single CECE process configuration can be designed to remove tritium from heavy water or light water and upgrade heavy water. Such a design would have some restrictions on the nature of the feed-stock and tritium product, but could produce essentially tritium-free light or heavy water that is chemically pure. The extracted tritium is produced as a small quantity of tritiated heavy water. The overall plant capacity is fixed by the total amount of electrolysis and volume of catalyst. In this proposal, with 60 kA of electrolysis a throughput of 15 kg·h−1 light water for detritiation, about 4 kg·h−1 of heavy water for detritiation and about 27 kg·h−1 of 98% heavy water for upgrading can be processed. Such a plant requires about 1,000 L of AECL isotope exchange catalyst. The general design features and details of this multi-purpose CECE process are described in this paper, based on some practical choices of design criteria. In addition, we outline the small differences that must be accommodated and some compromises that must be made to make the plant capable of such flexible operation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".