Fractional order models for soybean drying kinetics: A mathematical approach
Bibliographic record
Abstract
Abstract This study presents an advanced mathematical modelling approach to analyze the drying kinetics of Monsoy 6410 soybeans at temperatures of 50, 60, 70, and 80°C. Experimental data were obtained using a fixed‐bed dryer. Classical drying models were evaluated and compared with the Friesen fractional‐order model, which was enhanced by introducing a moisture‐dependent kinetic constant k ( X ). The modelling was performed using R software. Statistical comparisons using Akaike information criterion (AIC), Bayesian information criterion (BIC), mean squared error (MSE), and root mean squared error (RMSE) demonstrated the superiority of the fractional‐order models over classical ones. For example, at 50°C, the fractional‐order model with linear k(X) achieved AIC = −374, MSE = 6.98 × 10 −8 , and RMSE = 2.64 × 10 −4 , outperforming the Verma model (AIC = −342, RMSE = 4.55 × 10 −4 ). Across all temperatures, the fractional model with linear k(X) provided the best fit in 3 of 4 cases. This confirms the model's ability to capture complex drying behaviour more accurately, especially at high temperatures, where non‐linear and memory effects are significant. The results demonstrate the importance of fractional calculus in improving drying kinetics modelling and optimizing agro‐industrial drying processes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".