Synthesis of Rice Processing Plants. III. Sensitivity Analysis
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Bibliographic record
Abstract
Abstract An optimal process flowsheet for a rice processing plant has been developed. The optimization problem was formulated as a Mixed-Integer Nonlinear Programme, MINLP, consisting of a vector of binary and continuous variables. The set of optimum decision variables including the number of drying, cooling, and tempering units, temperature and relative humidity of drying air, drying time, cooling time, and tempering time were determined as the solution of the MINLP problem. Six objective functions were investigated as performance criteria. A sensitivity analysis of each model parameter was conducted to determine its influence on the optimal flowsheet. The MINLP approach is an efficient tool for optimization and the simplified models were adequate for use at the synthesis stage. The solution is insensitive to uncertainty in the models. However, due to the nonconvex nature of MINLPs, the solution was found to depend on the initial starting values, especially for the maximum profit flowsheet. Keywords: OptimizationMINLPDryingModels
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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 | 0.000 | 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