Optimizing profitability of batch fermentation processes using machine learning‐based endpoint prediction
Bibliographic record
Abstract
Abstract The accurate determination of production duration remains challenging in real‐time batch operations, making it difficult to optimize the termination time with minimal time and material consumption. To enhance batch profitability, this work develops a machine learning‐based approach for online batch endpoint identification. In this framework, quality‐related variables are used to represent the batch duration, and a data‐driven soft sensor is established to estimate these variables. Given the complex nonlinear dynamics and significant inter‐batch variability in batch processes, a dedicated recurrent neural network is designed with a time‐weighted loss function to improve prediction accuracy in the later stages of production. Furthermore, a decision‐making strategy combined with a Savitzky–Golay (S–G) filter is introduced to robustly identify the optimal endpoint for each batch. The proposed method is validated through an industrial‐scale penicillin fermentation process. Results demonstrate that the batch operation can be terminated approximately 58.5 h earlier on average, leading to a 27.68% increase in productivity and saving about 10,157 RMB in material costs per batch.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".