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Record W7133047187

Development of a Mathematical Model for Monitoring Recovery Boiler Dissolving Tank Sounds

2022· dissertation· W7133047187 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

VenueTSpace · 2022
Typedissertation
Language
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDissolutionPulp millMillBoiler (water heating)Kraft paperImpellerDissolving pulp
DOInot available

Abstract

fetched live from OpenAlex

In the chemical recovery process of kraft pulp mills, molten smelt falls into the dissolving tank where it interacts violently with hot water. These smelt-water interactions allow for fast smelt dissolution, however too many violent interactions can also cause equipment damage. In severe cases, violent smelt-water interactions may result in dissolving tank explosions, costing millions of dollars to pulp mills. One way to monitor smelt-water interactions within the dissolving tank is through the sound they generate. In this work, an acoustic model of smelt-water interaction was developed to examine dissolving tank sound characteristics and operating factors affecting the sound intensity. Field studies were conducted to obtain acoustic data at several mill sites. Laboratory experiments were then conducted to study each part of the smelt-water interaction process. The results of field measurements and laboratory experiments allowed for better understanding of the physical mechanisms involved in smelt-water interactions in the dissolving tank. This model is stochastic in nature and describes the physical processes from the moment molten smelt droplets enter water to the acoustic signals produced by numerous vapour bubble expansions and collapses. Each component of the model was verified through empirical data. The simulation results of the integrated model were then compared against acoustic measurements taken from mill visits. The model predictions were in good agreement with the sounds recorded from pulp mills under various operating conditions. The model could also accurately predict other mill variables such as the temperature of green liquor in the dissolving tank based on acoustic signals. In addition, the model provides predictions of changes within the dissolving tank when parameters such as smelt droplet size distributions and smelt flow rate are varied. The results obtained through these simulations show trends that are in agreement with findings from other studies. The results also suggest that dissolving tank water temperature, smelt flow rate, and smelt droplet size are amongst the most important factors in the intensity of explosion events. The model and algorithmic procedures developed in this thesis work may be used to develop an acoustic monitoring system for recovery boiler dissolving tanks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
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 head0.048
GPT teacher head0.319
Teacher spread0.271 · 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