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Record W2337907657 · doi:10.1002/9781118534892.ch4

Energy and Exergy Analyses of Drying Processes and Systems

2015· other· en· W2337907657 on OpenAlexaff
İbrahim Dinçer, Calin Zamfirescu

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsExergyExergy efficiencyProcess engineeringFluidized bedWaste managementCombustionEnvironmental scienceEnergy balanceEnergy flowEnergy (signal processing)EngineeringThermodynamicsChemistryMathematics

Abstract

fetched live from OpenAlex

This chapter presents the energy and exergy analysis of drying processes and systems. First, the balance equations and other equations relevant to modeling and analysis of drying processes through energy and exergy methods are introduced. Next, the efficiency definitions which are specific to drying systems and other assessment parameters are given. Then, the chapter presents an analysis, based on energy and exergy methods, of continuous-flow direct combustion dryers. With a heat pump dryer, an excellent match of temperature levels between supply side and demand side can be obtained. Minimization of the temperature differences leads to reduced exergy destructions. Fluidized bed dryers are very important in the industry when granulate materials or powders must be dried. The chapter presents the energy and exergy analyses for fluidized bed dryers.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.587
Threshold uncertainty score0.994

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 head0.083
GPT teacher head0.275
Teacher spread0.193 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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