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Record W2623713796 · doi:10.5383/ijtee.09.02.007

Design, Construction and Operation of Solar Dryer for Granules and Micros Chips of Manihot esculenta crantz Tuberous Roots

2015· article· en· W2623713796 on OpenAlexvenueno aff
P Bokungu Efoto, Louis Efoto Eale, Mahungu Meso

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
FundersUnited States Agency for International Development
KeywordsManihot esculentaMoistureHumiditySolar dryerMaterials scienceMathematicsHorticulturePulp and paper industryWater contentComposite materialPhysicsEngineeringMeteorologyBiology

Abstract

fetched live from OpenAlex

This study presents the design details and the performance analyses carried out with Eloumah 1, the solar drier of cassava tuberous roots reduced in granules and microchips. Eloumah 1 is composed by a solar collector, a drying chamber and a box of rectangular section that joins the two previous components. In this solar drier the wet agricultural products are dried on the basis of the heat flux buoyancy that is induced by the difference in temperature and humidity in its compartments. Analyses of masses evaporated water of zizila and Obama (TME 419) granules and microchips tuberous roots varieties have been carried out in order to estimate the drying performance of Eloumah 1 and to know the natural laws of the drying process. The results show that Eloumah 1 is able to dry granules and microchiphs and to reduce their moisture contain to less than 10%. Moreover, it can be established that the drying process is a logistic process because in wet control samples, moisture contain has limited value. Therefore, the drying process cannot extract the free water beyond this limit value. The logistic function adjusts well these data based on the correlation coefficient (R2) and chi square coefficient (χ2).

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 score0.259
Threshold uncertainty score0.104

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 head0.018
GPT teacher head0.188
Teacher spread0.170 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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