Optimization of Passivation and Cooling Water System Treatment of Brass Alloys in Petrochemical Facilities
Bibliographic record
Abstract
Abstract Real-world plant experience along with laboratory experimental studies is being used to optimize the corporate cooling water systems. All aspects of the program are being considered, including passivation treatments for individual bundles, system pre-film treatments, and long-term cooling water chemistries. The aim of the optimization plan is to select the most favorable combination of passivation, pre-film, and cooling water solutions that provides the most cost-effective protection of the system within defined performance targets. Electrochemical techniques are being used to investigate the passivation, system pre-film, and cooling water solutions and how they interact with each other. This paper summarizes the results of electrochemical laboratory studies designed to understand the pre-film treatment and cooling water circulation treatment of Naval brass heat exchangers with different azole-based inhibitors. The effect of operational parameters, i.e., temperature, pH, and inhibitor concentration, on inhibiting effect of inhibitors is studied for pre-film treatment. Single azole-based inhibitors along with mixed inhibitors are evaluated in synthetic cooling water for cooling water circulation treatment. Also, the effect of chloride in synthetic cooling water is discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".