Mathematical modeling and regression analysis using MATLAB for optimization of microwave drying efficiency of banana
Why this work is in the frame
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Bibliographic record
Abstract
This study examined the influence of microwave (MW) power (300–800 W) and standard geometries (slab, cube, disc, sphere) on banana drying kinetics . Nine thin-layer models were explored. Model validation employed the coefficient of determination and root mean square errors, further checked using residual sum of squares and zero mean of errors. The Henderson and Pabis model emerged as optimal for thin-layer drying. As MW power ranged from 300 to 800 W, drying times reduced: slabs (700–220 sec), cubes (800–300 sec), discs (560–160 sec), and spheres (620–200 sec). Moisture diffusivities spanned 9.12×10 −9 to 7.2×10 −6 m 2 /s, revealing reduced moisture diffusion at lower MW power. Activation energies were tabulated for discs (25.54 W/g), spheres (108.96 W/g), cubes (88.41 W/g), and slabs (107.56 W/g). Sample mass uncertainty was approximated at 0.03 g (∼1% error). Specific energy varied between 140 and 244 kJ, while microwave energy values ranged from 44.58 to 112.8 MJ/kg, declining with increased power. Cube samples showed maximum energy consumption, whereas disc samples typically consumed the least. Notably, disc-shaped bananas demonstrated peak drying energy efficiencies (41.75–51.3 %) across all MW power levels. Average drying efficiencies were observed between 19.97 and 51.3 % for the studied power range.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it