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Record W2012755478 · doi:10.1002/cjce.5450800221

An optimization model of the operating costs of a fluidized bed steam‐drying plant

2002· article· en· W2012755478 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.

venuePublished in a venue whose home country is Canada.
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

VenueThe Canadian Journal of Chemical Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsSuperheated steamFluidized bedMass transferProcess engineeringMoistureWater vaporSuperheatingProcess (computing)Waste managementEnvironmental scienceMechanicsPetroleum engineeringThermodynamicsMaterials scienceEngineeringComputer sciencePhysicsMeteorology

Abstract

fetched live from OpenAlex

Abstract A model is developed to determine an optimum apparatus geometry and, for given apparatus dimensions, a financially optimal fluidized bed height. The parameters that effect the operating costs are the bed mass, the apparatus diameter and the gas mass flow rate. To implement such cost optimization, a physics‐based mathematical model for describing the thermodynamic processes in fluidized bed steam‐drying is briefly explained and presented. The most important conclusion is not to operate the fluidized bed for a drying process below a certain minimum cost, calculated with the help of the modelling. The problem, when describing the drying process and consequently the mass transfer, is that in the superheated steam drying case studied here, water is evaporated as moisture and withdrawn into an atmosphere of vapor water.

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 score0.150
Threshold uncertainty score0.264

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 head0.012
GPT teacher head0.176
Teacher spread0.165 · 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