FUEL CYCLE IMPLICATIONS OF DEPLOYING HTGRS IN HYBRID ENERGY SYSTEMS AS RESERVE POWER GENERATION IN ONTARIO
Bibliographic record
Abstract
Nuclear power plants could potentially be deployed in a type of nuclear hybrid energy system (NHES) in which their power is used primarily to drive an industrial process but can be diverted to meet demands for electricity when needed. The purpose of this study is to analyze the effects of deploying NHESs as reserve power for the transmission grid in Ontario on the overall Canadian fuel cycle. In this scenario, the fuel cycle demands of 2 high-temperature gas-cooled reactor (HTGR) concepts are analyzed with respect to costs, resource consumption, and enrichment requirements. One HTGR concept is a 30 MW-thermal (MW th ) reactor that is based on the UBattery concept, and the other is the Xe-100, which is a 200 MW th reactor. Calculations indicate that such a deployment of HTGRs would have a substantial effect on the fuel cycle in Canada. In particular, NU and enrichment demands would be greatly affected. Beginning this HTGR deployment in the year 2030 would more than double the annual NU demands in Canada, and deplete the uranium resources with extraction costs of <$80/kgU by the year 2142. The uranium enrichment demands of this fleet would be >35% of the US capacity for uranium enrichment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".