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Record W3184977222 · doi:10.5539/eer.v11n2p19

Energy and Exergy Analysis of an Indirect-Mode Natural Convection Solar Dryer for Maize

2021· article· en· W3184977222 on OpenAlexvenueno aff
Isaac N. Simate

Bibliographic record

VenueEnergy and Environment Research · 2021
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsExergyExergy efficiencyEnvironmental scienceSolar dryerEnergy analysisGrain dryingMoistureSolar energyMaterials scienceEnvironmental engineeringWaste managementEnergy (signal processing)Composite materialMathematicsEcologyEngineering

Abstract

fetched live from OpenAlex

The energy and exergy analysis of an indirect-mode natural convection solar dryer for maize grain is presented. Two different sizes of maize grain bed depths of 0.04 m and 0.02 m translating into grain loads of 10 kg and 5 kg respectively, are used in the study to determine their effects on the collector energy and exergy efficiencies and the drying chamber exergy efficiency. Experiments were carried out using an indirect-mode laboratory solar dryer under a solar simulator with a radiation setting of 634.78 W/m2. The analysis gave average collector energy efficiencies of 33.3 % and 46.2 % for the 10 kg and 5 kg loads, respectively, which are higher than the collector exergy efficiencies of 2.4 % and 2.6 % for the 10 kg and 5 kg loads, respectively. The drying chamber exergy efficiencies are 45.2 % and 28.4 % for the 10 kg and 5 kg loads, respectively. In view of this, the 5 kg load is considered to be more efficient at extracting energy from the collector due to higher air flow resulting from its relatively thin grain bed depth of 0.02 m, but less efficient in utilising the extracted energy to evaporate moisture from the grain which has resulted in a lower drying chamber exergy efficiency. Further, the exergy loss in the drying chamber for the 5 kg load is higher than that in the 10 kg load as 72.3 % of the exergy entering the drying chamber is lost through emissions as well as destroyed through internal irreversibility compared to 57.0 % for the 10 kg load. 

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.998

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 head0.028
GPT teacher head0.286
Teacher spread0.258 · 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
Published2021
Admission routes1
Has abstractyes

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