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Record W4377195072 · doi:10.1002/cjce.24965

Numerical analysis and differential push‐flow structure optimization of vacuum disc drying process

2023· article· en· W4377195072 on OpenAlexvenueno aff
Kaixin Lv, Hongmei Liu, Xuedong Liu, Youheng Zhou, Wei Jiang, Weiwen Zheng, Honghong Zhang, Hui Chen, Ke Shao, Zhiqiang Xu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangsu ProvinceGovernment of Jiangsu Province
KeywordsMaterials scienceFlow (mathematics)Process (computing)Volumetric flow rateDifferential (mechanical device)MechanicsStage (stratigraphy)Mechanical engineeringComposite materialEngineeringComputer sciencePhysicsThermodynamicsGeology

Abstract

fetched live from OpenAlex

Abstract The drying characteristics of sludge in a uniaxial vacuum disc dryer are numerically studied in the present study, and the results showed that the drying process of sludge includes three stages: the viscous stage, the sticky stage, and the granular stage. The influence of main parameters such as sludge feeding rate, heat source temperature, and rotating speed of the shaft on the drying process of sludge in the uniaxial vacuum disc dryer is analyzed. In order to optimize the drying efficiency of the dryer, a differential push‐flow structure is proposed in this study, which provides different propulsion effects for the sludge in different drying stages. The differential push‐flow is achieved by changing the number and angle of the push‐flow blades on the discs. Compared with the dryer before optimization, the dryer with optimized differential push‐flow structure has better performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.114

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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 designSimulation or modeling
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

Citations3
Published2023
Admission routes1
Has abstractyes

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