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Record W2980265515 · doi:10.32964/tj15.9.591

Acoustic analysis of recovery boiler dissolving tank operation and smelt shattering efficiency

2016· article· en· W2980265515 on OpenAlexfundno aff
Hugo V. Lepage, Willy Wong, Markus Bussmann, Honghi Tran

Bibliographic record

VenueTAPPI Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsBoiler (water heating)DissolutionSmeltEnvironmental scienceAcousticsWaste managementEngineeringPetroleum engineeringFishery

Abstract

fetched live from OpenAlex

The interaction of molten smelt and water inside a recovery boiler dissolving tank produces loud noise and can be violent even during normal boiler operation. Inadequate shattering of the smelt stream leads to even more violent interaction, as evidenced by an increased acoustic intensity of the dissolving tank. On rare occasions, a violent dissolving tank can explode, causing equipment damage and even injury to personnel. To warn operators of changes in dissolving tank conditions, an acoustics-based monitoring system could be developed. To assess the feasibility of such a system, acoustic observations were recorded at three pulp mills. Analysis of the recordings indicates that when a smelt stream is not being shattered, the intensity of the dissolving tank soundscape increases significantly and the frequency spectrum changes. We also observed a large variation between different mills both in average intensity and in signal variance. The results of this study suggest that the development of a monitoring system is feasible.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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