Corrosion Monitoring and Root Cause Identification in High Solids Concentrators
Bibliographic record
Abstract
Abstract Black liquor high solids (about 80%) concentrators have often been found to suffer from aggressive corrosion. Especially the 1st and 2nd effect bodies are susceptible to corrosion attacks resulting in tube leaks and wall thinning, limiting the availability and lifetime of evaporator lines. Corrosion dynamics and utilized construction materials have been studied extensively within the pulp and paper industry in order to understand the corrosion process. However, it has been challenging to identify root causes for corrosion, which has limited pro-active measures in minimizing corrosion damages. In this case, corrosion of the 1st phase concentrator was studied by defining the potential regions for passive area, stress corrosion cracking, pitting corrosion and general corrosion. This was achieved by using a technique called Polarization Scan which reveals ranges for the passive area in which the equipment is naturally protected against corrosion. The Open Circuit Potential (OCP) a.k.a. corrosion potential and Linear Polarization Resistance (LPR) of the metal were monitored on-line, which enabled to define corrosion risks for stainless steel 304L, duplex stainless steels 2205 and SAF 2906. An on-line temperature measurement complemented the results adding valuable insight to the analysis. A powerful process diagnostics tool, WedgeTM, was used to identify root causes of the corrosion attacks. Many of the root causes were related to process conditions triggering corrosion. Once the metal surface was activated, it was difficult to re-passivate the metal naturally unless a sufficient potential range was reached. The project enabled the mill to study various process scenarios and find a less corroding formula for the process. Ultimately, the mill was able to reduce corrosion risks and hence extend the life time of the vessels.
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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".