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Record W2766566435 · doi:10.5539/eer.v7n2p37

Thin Layer Mathematical Modelling of Cob Maize in a Natural Convection Solar Drier

2017· article· en· W2766566435 on OpenAlexvenueno aff
Mukuwa Mukwangole, Isaac N. Simate

Bibliographic record

VenueEnergy and Environment Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsRelative humidityWater contentMoistureConvectionExponential functionEnvironmental scienceHumidityNatural convectionThin layerMaterials scienceMeteorologyMathematicsLayer (electronics)Composite materialPhysics

Abstract

fetched live from OpenAlex

A semi- cylindrical natural convection solar tunnel drier (STD) was used for experiments to dry maize cobs that were partially field- dried. The air temperature recorded in the collector unit was higher than ambient temperature by 15.8oC to 20.8oC and this was effective in reducing the moisture content of maize cobs from 30 % wet basis to safe level storage moisture content of 12.5 % wet basis. Averages of solar radiation recorded during the experiments ranged from 585.6 W/m2 to 759.8 W/m2. The averages of relative humidity that prevailed in the drying unit ranged from 6.8 % to 18.2 % whereas the ambient relative humidity averaged from 27.3 % to 43.7 %. Drying time was examined with moisture content ratio as exponential and polynomial correlations. Fourteen different drying mathematical models available in literature were compared using their coefficients of determination to estimate solar drying curves. Based on statistical analysis of the results, Midilli drying model had the best fit to the experimental drying data of maize with a coefficient of determination R2= 0.99912 as compared to other models.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.112
GPT teacher head0.288
Teacher spread0.175 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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