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Record W85240683 · doi:10.5006/c2004-04078

Unique Chemistry and Control Technology Merge to Improve Cooling Water Performance and Control

2004· article· en· W85240683 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsNalcor Energy (Canada)Nalco (Canada)
Fundersnot available
KeywordsMerge (version control)Water chemistryControl (management)CorrosionComputer scienceProcess engineeringChemistryMaterials scienceEngineeringMetallurgyEnvironmental chemistryArtificial intelligenceParallel computing

Abstract

fetched live from OpenAlex

Abstract An automated control and monitoring program has been deployed at an open recirculating cooling water system within a west coast refinery plant. The comprehensive monitoring program applied several fluorescence-based molecules as a means to determine appropriate dosage and consumption of cooling water chemicals. In the meantime, the fluorescence sensor molecules also provided information regarding microbial activity in the cooling water to trigger biocide feed control. All the fluorescence signals and system operating parameters were analyzed by a multi-channel fluorometer and its built-in control algorithm for an interactive system control on corrosion, scale and microbial fouling. This comprehensive cooling water control scheme has revolutionized the conventional industrial water treatment strategy. The new 24/7 control package has resulted in improving the aspects of “cooling water triangle” and provided immediate mitigation of system upsets to ensure an uninterrupted plant product.

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.

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 score0.061
Threshold uncertainty score0.575

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)0.0000.000

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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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