Reducing chloride corrosion of stainless steel in the nuclear fuel manufacturing industry : an electrochemical-environmental perspective
Bibliographic record
Abstract
Chloride extraction from nitric acid is an important technique for reducing corrosion of stainless steel. However, there has been a limited amount of research conducted in this area. Pumping ozone-enriched air through nitric acid is a corrosion reduction method that is widely used in the nuclear fuel manufacturing industry, including the Blind River Refinery (BRR), to purge chlorine gas out of the acid. However, this method has been shown to produce significant environmental impacts. Overall, it is an inconsistent and cost-deficient method for reducing chloride corrosion of stainless steel in nitric acid mediums below 7.2M (37.0% volume). This thesis builds on existing literature and demonstrates that oxidizing chloride ions in nitric acid using oxygen, nitric oxide and nitrous oxide is an efficient and cost-effective chloride extraction method for the case study (BRR). It was shown that the level of chloride extraction from nitric acid increased significantly when the acid strength was elevated above 8.4M (42.0%volume) and sparged with various oxidants. The most effective oxidants at this nitric acid strength were: oxygen, ozone, nitric oxide and nitrous oxide. Nitric oxide and nitrous oxide can be produced by sparging 43.0% nitric acid with air or sparging 43.0% nitric acid with NOx fumes. In terms of the BRR case study, it was shown that using operational-specific combinations of these methods can drastically reduce the environmental impacts associated with their chloride removal process; significantly increase the level of chloride extraction; reduce energy consumption and operating costs by as much as 54.0%; and reduce material requirements by as much as 80.0%.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".