Development of a simultaneous continuum and noncontinuum state estimator with application on a distillation process
Bibliographic record
Abstract
Abstract This study focuses on the development and application of a hybrid moving horizon estimator (HMHE) to achieve simultaneous estimation of both continuum and noncontinuum states in a constrained switching dynamic system. One of the major issues in a moving horizon estimation approach is the development of an arrival cost to summarize the effect of past and a prior information. In this work, we have developed a generalized arrival cost, which accounts for both continuum and noncontinuum states. A generalized hybrid state estimator, which can be used as a stand‐alone continuum state estimator, or as a simultaneous continuum and noncontinuum state estimator, is developed. The effects of constraints, process, and measurement noise levels, as well as a moving horizon length on the simultaneous estimation of both the continuum and noncontinuum states are analyzed. The effectiveness of the HMHE is demonstrated through simulation studies, while its practical reliability is tested by conducting experimental studies on a distillation column. © 2011 American Institute of Chemical Engineers AIChE J, 2012
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".