ICONE23-1734 HITACHI-GE-SMALL MODULAR REACTOR BALANCE OF PLANT THERMAL UTILIZATION STUDY
Bibliographic record
Abstract
The Province of Saskatchewan and Hitachi Canada have established a collaborative relationship to research design options and conduct feasibility analyses of Small Modular Reactor (SMR) technology with the goal of safely and reliably generating clean energy and helping to achieve a low-carbon society. The focus of this study is to optimize the design of an SMR Balance of Plant (BOP) for the supply of steam and heat to various residential, industrial and commercial applications. The study includes a review to examine if waste heat available from the proposed SMR could be used for producing potable water using nuclear desalination technology. It is in the context that large volumes of groundwater and brackish water are available in Saskatchewan, Canada. The existing literature on desalination processes are being reviewed, including technologies for water production such as Multiple Effect Distillation (MED), Multi Stage Flash (MSF), and Reverse Osmosis (RO). The review includes comparison of various technologies for energy efficiency and overall economics of the process. The Desalination Economic Evaluation program (DEEP) computer model available from International atomic energy agency (IAEA) is also used as a tool for examination. Factors such as salinity of the feedwater, advantages gained by pre-heating the feedwater and efficient utilization of waste heat generated in the SMR showed that technology based on RO will be the most cost effective technology. The conclusions are also supported in previous reviews done by GE and AMEC for the government of Alberta, Canada. The quality of product water produced using RO technology depends on impurities in the feed water. Therefore, trials in a pilot plant and in a semi commercial plant are proposed as next steps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".