Optimization of L‐sorbose allocation for 2‐keto‐L‐gulonic acid production
Bibliographic record
Abstract
Abstract A fuzzy logic‐based optimal scheduling strategy was used to maximize the profitability of the 2‐keto‐L‐gulonic acid (2‐KGA) production process. In this work, profit function is predicted by rolling learning‐prediction (RLP) based on support vector machine (SVM). By combining the optimal scheduling strategy, the batches are online‐classified according to the results of the profit function. The fermentation time of secondary feeding was predicted by fuzzy logic method. Through three consecutive months of experiments, 90 historical 2‐KGA batches were run to verify the optimal scheduling method. The total profit increment of 749,630 PU is obtained for the workshop. The results indicate that 5.25% increase in total workshop profit compared to the empirical model.
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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".