Parameter Estimation for Batch Processes with Measurements of Large Sampling Intervals**This work is supported by Chang Jiang Scholar Program and National Natural Science Foundation of China (NSFC 61134007).
Bibliographic record
Abstract
Parameter estimation of batch processes is investigated in this paper. Due to the lack of online sensors and short length of each batch, very few off-line laboratory analysis measurements are available with a large sampling interval within each batch. To capture the nonlinear and non-Gaussian features, dual particle filters are employed to perform the state estimation and the parameter estimation in parallel. Different from most of conventional methods for the parameter estimation which only employ the measurements along the time dimension (measurements of a single batch), the measurements along the batch dimension are also taken into account in this work. In the proposed method, to avoid the estimation degeneracy due to few measurements available along the time dimension, the parameter is treated as an invariant along the time dimension and its variability is introduced along the batch dimension denoted by a random walk model. Considering that different batches share the same raw material, states among different batches are related by slowly varying or constant initial states. The application in a beer fermentation process is used to illustrate the proposed approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".