On‐line estimation of glucose and biomass concentration in batch fermentation process using particle filter with constraint
Bibliographic record
Abstract
ABSTRACT In most of fermentation processes, the substrate and biomass concentrations greatly influence the yield of the targeted product. In this paper, considering the nonlinear and non‐Gaussian nature of the fermentation processes, the particle filter method is introduced to estimate the substrate and biomass states, and an algorithm integrating optimization strategy with resampling is proposed to deal with constraints on states. The proposed approach investigates the constraint directly on the estimated value rather than on particles. If the estimation violates constraints, the optimal estimation is firstly obtained by the optimization approach, and then, the resampling and optimization strategy are iteratively used to randomly draw a particle each time among the violated posterior particles and project it onto the feasible region. The resampling and optimization steps are iterated until the state estimation, derived from posterior particles, reaches better performance than that through previous optimization formulation or all the violated posterior particles have been projected onto the feasible region. Compared with the other previous optimization approaches, the proposed method results in a better balance between reducing the on‐line computation load and obtaining more effective posterior particles. © 2012 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.000 | 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".