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Record W3023223864 · doi:10.5006/c2013-02255

Optimization of Passivation and Cooling Water System Treatment of Brass Alloys in Petrochemical Facilities

2013· article· en· W3023223864 on OpenAlexaff
Yan Li, Katy Yazdanfar, Chris Friesen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetallurgy and Material Science
Canadian institutionsNova Chemicals (Canada)
Fundersnot available
KeywordsBrassPetrochemicalPassivationMetallurgyMaterials scienceCorrosionWater coolingWaste managementEngineeringMechanical engineeringComposite materialCopper

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.208
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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