State Estimation in Batch Process Based on Two-Dimensional State-Space Model
Bibliographic record
Abstract
Most existing methods for the state estimation in batch processes are similar to those for continuous processes, and these methods usually only consider the state dynamics within a single batch and ignore the dynamics across batches. In this paper, the state estimation in batch processes is investigated based on a two-dimensional state-space model by employing the Bayesian recursive algorithm. In addition to the dynamics along the time dimension (the dynamics within a single batch), the batch process is also characterized by the dynamics along the batch dimension (batch-to-batch dynamics). In the proposed method, both the batch-to-batch dynamics and the dynamics within a single batch are taken into account. The current state is dependent on the previous states both along the time dimension and along the batch dimension, so the filtering and smoothing for previous batches should be performed before doing current state estimation. In this way, the information on measurements from the previous batches as well as from the current batch can be incorporated into the estimation. The proposed method is illustrated and evaluated through a simple numerical example as well as a simulated two-state batch reaction process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".