MétaCan
Menu
Back to cohort
Record W2972523704 · doi:10.5539/jas.v11n16p80

Proposal Solar Drying With Heat Storage Applied to Medicinal Plants

2019· article· en· W2972523704 on OpenAlexvenueno aff
Valentin Silvera Diaz, Eduardo Gonçalves Reimbrecht, Tales Jahn, Oswaldo Hideo Ando

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
FundersUniversitas Lampung
KeywordsHeat exchangerEnvironmental scienceSolar energyThermal energy storageWork (physics)Fraction (chemistry)ThermodynamicsMeteorologyAtmospheric sciencesNuclear engineeringPhysicsChemistryEngineering

Abstract

fetched live from OpenAlex

In this work, a proposal of an indirect solar dryer with a vacuum solar collector and storage of water-sensitive heater is presented. A mathematical model is presented as the first approximation to evaluate the performance. The test climatic conditions are based on the city of Cascavel (Paraná, Brazil) and the resolution of the model was aided by the EES program. In order to analyze the performance of the system, a model load was created based on the references found in the literature, sample of 80 kg with initial water content of 75% w.b and final of 10% w.b, for drying time of 4 h and 3 h with air velocity of 0.7 m/s and 1.1 m/s respectively. It was possible to simulate the variation of the temperature of the reservoir during the day, as well as the response of the heat exchanger to the variation of temperature of entrance of the fluids and the climatic influence, radiation and ambient temperature in the participation of the solar energy in the total energy consumption of the drying. The simulations suggest good results with solar fraction between 20 and 47 %, meanwhile in the literature the values reported are between 10 and 25%.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.209
Teacher spread0.198 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicFood Drying and ModelingFrench-language works237,207