Correlation of Process Data and Electrochemical Noise to Assess Kraft Digester Corrosion: Kamloops Experiment
Bibliographic record
Abstract
Electrochemical noise (ECN) probes were deployed in a carbon steel continuous kraft digester at five locations roughly equi-spaced from top to bottom of the vessel. Current and potential noise, the temperature at each probe location, and the value of about 60 process parameters (flow rates, liquor chemistry, etc.) were monitored continuously for a period of one year. Historical vessel inspection data, including inspections accomplished immediately prior to and immediately following probe deployment, and post-test evaluation of the probe components were used to assess/compare corrosion indications from the probes with physical changes in wall thickness and corrosion patterns on the digester shell. The results indicate that furnish composition is a significant variable influencing digester corrosion, with increasing amounts of Douglas fir in the nominal furnish correlating directly with increased corrosion activity on the ECN probes. All five probes detected changes in furnish composition approximately simultaneously, indicating rapid chemical communication through the liquor, but the effect was strongest and persisted longest relatively high in the digester. The ECN probes also indicate significant corrosion activity occurred at each probe position during shutdown/restart transients. Little or no correlation between ECN probe corrosion activity and other operational variables was observed. Post-test evaluation of the probes confirmed general corrosion of a magnitude that closely agreed with corrosion current sums calculated for each probe over the exposure period and with historical average corrosion rates for the respective locations. Further, no pitting was observed on any of the electrodes, which is consistent with the ECN data, relevant polarization curves developed for steel in liquor removed from the digester, and the post-test inspection of the digester.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".