Generalized Disjunctive Programming for Synthesis of Rice Drying Processes
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Bibliographic record
Abstract
Rice drying synthesis is an essential operation that has to be done carefully and cost-effectively. Fast drying can cause fissuring, which lowers the market value of the rice grains. Multipass drying systems are therefore used to bring the moisture content to desired levels gradually. To determine the best configuration of units and their corresponding operating conditions that maximize rice quality and minimize energy consumption, empirical models are used. However, empirical models have limited ranges of validity. Moreover, different mathematical models are possible for the same synthesis problem. This paper proposes a generalized disjunctive programming (GDP) framework for the synthesis problem of rice drying in order to increase the overall range of applicability of the empirical models and establish a consistent solution strategy. The proposed framework is investigated and tested on several case studies. Different drying strategies resulted from solving the synthesis problem with different empirical models, providing us with a broader vision of the mechanism of rice drying processes. The results indicate that the GDP framework can facilitate the modeling of the synthesis problem and increase the efficiency of optimization algorithms.
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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 | 0.005 |
| 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 | 0.001 |
| 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