Neural and Hybrid Neural Modeling and Control of Fed‐Batch Fermentation for Streptokinase: Comparative Evaluation under Nonideal Conditions
Bibliographic record
Abstract
Abstract Fermentations involving competition between two or more kinds of cells under nonideal conditions show complex profiles that are sensitive to the extra‐cellular environment. These fermentations therefore require accurate and rapid on‐line data acquisition and control. However, both on‐line measurements and modelling are difficult and expensive for large bioreactors, thus limiting the usefulness of model‐based control. While neural networks offer an alternative, they require extensive training and can be difficult to optimize for large arrays. Hybrid networks combining a few neural networks with some mathematical equations offer a good compromise. The possibility of using a hybrid model for simulation‐cum‐control has been examined here for the fed‐batch production of streptokinase. Under noideal conditions, hybrid neural models outperformed both mathematical models and arrays of neural networks, thus suggesting their viability for large‐scale fermentation monitoring and control.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".