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A Mathematical Model for Dehydration by Successive Pressure Drops: Simulation of Discarded Potatoes Dehydration

2021· article· en· W3202345979 on OpenAlexafffund
Sebastián Gutiérrez-Pacheco, Joahnn H. Palacios, Alfonso Parra-Coronado, Stéphane Godbout

Bibliographic record

VenueJournal of Food Technology Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsInstitut de Recherche et de Développement en AgroenvironnementUniversité Laval
FundersUniversidad Nacional de ColombiaUniversité Laval
KeywordsDehydrationKineticsDrop (telecommunication)Mathematical modelThermodynamicsPressure dropMaterials scienceProcess (computing)Process engineeringChemistryMechanicsMathematicsMechanical engineeringComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Dehydration by Successive Pressure Drops (SPD) is a process with high potential for treating heat-sensitive materials; this includes agro-industry by-products. However, the response of drying kinetics to operating conditions of SPD is not fully understood. The present manuscript refers to mathematical simulations to describe drying kinetics of discarded potatoes using SPD. While there are numerous theoretical, semi-theorical and empirical mathematical models, the selection of the appropriate model is a rigorous process. In this paper, the Thompson thin-layer semi-theoretical model was chosen since the assumptions for this model (e.g., product is arranged in thin layers) are fulfilled for the SPD. As a result of mathematical simulation, it was possible to describe the drying kinetics in terms of the major parameters of SPD namely pressurizing level (Pa) and the Frequency of Pressure Drop (FPD). The dehydration by SPD allows the removal of water from the material, mainly at the pressure drop. If this stage occurs more frequently, the drying rate increases. The model developed describes at less 91% of the variability of the experimental data. It is recommended to use high FPD and Pa equals to 0.50 MPa.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.076
GPT teacher head0.358
Teacher spread0.282 · 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
Published2021
Admission routes2
Has abstractyes

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