Energy and Exergy Analysis of an Indirect-Mode Natural Convection Solar Dryer for Maize
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".