Synthesis of Rice Processing Plants. II. MINLP Optimization
Bibliographic record
Abstract
A systematic design approach was applied to develop the optimal process flowsheet for a rice processing plant. The optimization problem was formulated as a Mixed-Integer NonLinear Programme, MINLP, consisting of vectors of binary and continuous variables. A superstructure flowsheet comprising all serial structures of drying, cooling, and tempering units in the process was postulated. 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 determine as the solution of the corresponding MINLP. Six objective functions were investigated as possible performance criteria: production time, number of the operating units, energy consumption, total operating cost, head rice yield, and the profit. The choice of objective function was found to have a significant effect on the optimal solution. Comparison with typical design and operating conditions, the MINLP results showed that a 22% reduction in energy consumption was possible along with a 2.4% increase in head rice yield. These savings, if applied to the world-wide rice industry, translate into more than $3 billion dollars/year increase in profit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".