Corrosion of Alloys in a Simulated Wood-Waste Fluidizing Bed Power Boiler Environment
Bibliographic record
Abstract
Rapid corrosion of Type 310H (UNS S31009) stainless steel fluidizing bed nozzles was observed shortly after start-up of a fluidizing bed power boiler designed to combust a mixture of salt-laden, wood-waste (hogged-fuel), and sludge. The primary cause of corrosion was previously identified as active oxidation, a high-temperature chloridation reaction occurring under thick, chloride-rich ash deposit. Replacing the nozzle metallurgy with a more corrosion-resistant material was one life extension strategy pursued by the mill. To assist the mill in this regard, an accelerated laboratory-based screening test was developed and used to identify materials from which to fabricate nozzles for field testing in the fluidizing bed power boiler. The accelerated screening test was validated by its consistency with field observations, namely, the corrosion mode and rate exhibited by Type 310H stainless steel. Of the various materials evaluated, the nickel-based Alloy 625 (UNS N06625) and Alloy HR160 (UNS N12160) were the most resistant to corrosion in the simulated environments. Alloy 625 was found to be at least seven to 10 times more corrosion resistant than Type 310H stainless steel when sulfur dioxide (SO2) was present, and about 100 times more corrosion resistant in the absence of SO2. Based on these tests, four alternative materials were selected for field testing.
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".