Drying Kinetic Models of Rice Applying Fluidized Bed Dryer
Bibliographic record
Abstract
Rice is the main food in Indonesia. Rice drying by using the traditional method directly under the sun light can require a long time to complete. The aim of this study is to investigate the appropriate kinetics modeling of rice with applied by fluidized bed dryer. A rice bed with 2-cm thickness has been dried at various temperatures (50℃, 60℃, and 70℃) with air velocity of 10 m/s applied from hot air fluidized dryer obtained from pyrolysis process. The appropriate rice drying kinetics modeling has been selected based on the agreement between experimental results and seven drying kinetics equations available namely the drying kinetics modeling of Newton, Page, Henderson-Pabis, Logarithmic, Midilli, Two Term, and Verma. The degree of accuracy for the kinetics modeling is determined based on six statistics parameters namely the coefficient determination (R2), mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE), Akaike information criterion (AIC), and Schwarz information criterion (SIC). The results of the study show that the Verma drying kinetics modeling is the most appropriate model for rice using fluidized bed dryer with all given temperatures (50℃, 60℃, and 70℃) with regard to six given statistics parameters (R2, MAD, MSE, RMSE, AIC, and SIC).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".