Performance Analysis and Kinetic Modeling of Coffee Beans in Microwave Convective Dryer Integrated Photovoltaic System
Bibliographic record
Abstract
The microwave convective dryer integrated photovoltaic has the potential equipment to be used for coffee drying. Therefore, this study aims to examine the performance of microwave convective dryer integrated photovoltaic for coffee bean drying. The kinetics and effectiveness diffusivity were determined to describe the drying process”. Drying model for the coffee bean drying process using variations in sample mass, coffee bean condition, and drying power. The results showed that reducing the water content of coffee beans from 47% (wb) to 11% (wb) required drying times of 10, 8, and 6 minutes at 600 W (medium), 650 W (medium-high), and 700 W (high), respectively. Drying coffee beans with a microwave convective dryer system shows that the Wang and Singh model is the best model for drying coffee beans; resulting in the highest R2 (0.99) and the lowest MBE and RMSE. Therefore, Wang and Singh model can be used to accurately predict the moisture content of dry coffee beans in a microwave convective dryer system. The effective diffusivity of coffee beans increased from 3.19×10-7 m/s to 1.19×10-6 m2/s when the power drying increased from 600 W to 700 W.
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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) | 0.000 | 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".