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Record W2921530468 · doi:10.32964/tj16.10.597

Formation mechanisms of “ jellyroll” smelt in kraft recovery boilers

2017· article· en· W2921530468 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTAPPI Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsSmeltBoiler (water heating)Waste managementEnvironmental scienceEngineeringFisheryBiology

Abstract

fetched live from OpenAlex

Molten smelt normally flows smoothly down the smelt spout of a recovery boiler like water, but at times it suddenly becomes sluggish and forms a viscous blob on the spout trough that partially or completely blocks the smelt flow. This form of smelt is commonly referred to as “jellyroll” smelt. How such smelt forms has been a puzzle to boiler operators and mill personnel for years. Numerous mill observations and the results of a recent study performed on both smoothly flowing smelt and jellyroll smelt collected from a recovery boiler suggest that that jellyroll smelt can form through three main mechanisms: i) the freezing of the molten smelt, ii) the melting of fallen deposits, and iii) the inclusion of a large amount of unburned char in the molten smelt. These mechanisms are consistent with mill experience that jellyroll smelt tends to form in older recovery boilers burning liquor with low solids and low sulfidity.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.268

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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