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Record W2899144159 · doi:10.2298/ciceq180827030t

Analysis of heat and moisture transfer during drying of urea particles

2018· article· en· W2899144159 on OpenAlexaff
Bahram Torkashvand, Sina Gilassi, Reza Alipour Moghadam Esfahani

Bibliographic record

VenueChemical Industry and Chemical Engineering Quarterly · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCakingUreaMoistureMass transferCooling towerMaterials scienceTowerHeat transferWater contentEnvironmental scienceChemical engineeringMechanicsChemistryThermodynamicsComposite materialChromatographyEngineeringWater coolingOrganic chemistry

Abstract

fetched live from OpenAlex

Urea is an inexpensive form of nitrogen fertilizer which is widely used in the agricultural industry. Urea as granule is produced through a drying process by which moisture content decreases to avoid operational issues. In this study, a numerical model is proposed for the drying of urea particles in the prilling process. The model is developed based on mass, heat, and hydrodynamic transfer equations for the urea particles and cooling air. The moisture and temperature variations of particles and cooling air at different heights of the prilling tower are calculated under different operating conditions. The shrinkage of particles due to the moisture loss during the drying process at different heights of the tower is considered. The model is validated with the real data obtained from a urea drying plant operated under steady-state condition. The result highlights that the model can be used to manipulate the operating parameters to improve product quality and to minimize urea temperature to prevent lamps and caking formation in the tower.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.266

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 head0.195
Teacher spread0.183 · 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 designBench or experimental
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

Citations2
Published2018
Admission routes1
Has abstractyes

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