On-line estimation of glucose and biomass concentration in penicillin fermentation batch process using particle filter with constraint
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a penicillin fermentation process, substrate concentration and biomass concentration greatly influence the yield of the targeted product. However, there are few on-line sensors available to measure these variables in real-time. In this paper, a compact mechanism model is employed to simulate the fed-batch process, and a particle filter is introduced to estimate the substrate and biomass states. Particle filters are favorable to handle the state estimation problems with non-linearity, time-varying dynamics, and non-Gaussian distributions. In order to improve the quality of particles, optimization strategies are applied to deal with constraint issues. Furthermore, infrequent lab analyzed state information is incorporated into the estimation procedure and used to correct PF estimate. Simulation results show that the constrained PF approach has better estimation performance than extended Kalman filter in state estimation of this penicillin fermentation batch process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it