Autoassociative neural networks for robust dynamic data reconciliation
Bibliographic record
Abstract
Abstract Reliable estimation of process variables for plant monitoring and control is an important topic that has been studied extensively. The Kalman filter has often been used and has acquired an enviable reputation. However, use of the Kalman filter suffers from two restrictive conditions: (1) it requires state‐space models and (2) it has to be tuned online to achieve its best performance. Recently, an alternative methodology based on dynamic data reconciliation has been proposed to overcome the first restriction. Although the approach of dynamic data reconciliation can incorporate any form of model, it involves online optimization that may require long computation time for complex systems. This report explores a new methodology based on a combination of autoassociative neural networks (AANNs) and dynamic data reconciliation that overcomes the need for online tuning, as required by the Kalman filter, as well as online optimization as required by conventional dynamic data reconciliation methods. Simulation examples of a distillation column demonstrate that the AANN‐based dynamic data reconciliation approach is capable of effectively attenuating measurement noise and is robust to changes of the noise level in plant measurements and the loss of measurements. © 2007 American Institute of Chemical Engineers AIChE J 2007
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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 | 0.000 |
| 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 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".