Carbon capture plant model identification through simultaneous state and parameter estimation with estimable variable selection
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
This paper addresses the challenge of estimating both states and parameters for post-combustion carbon capture plants (CCPs), with the goal of predicting CO 2 capture using temperature measurements. We develop a first-principle model of the CCP, modified to align with the actual industrial process, and employ simultaneous state and parameter estimation within a moving horizon estimation (MHE) framework. Sensitivity analysis and orthogonalization are used in variable selection step to select estimable states and parameters, enhancing estimation accuracy and computational efficiency. Real industrial data is used to validate the model, and comparisons with alternative estimation methods highlight the effectiveness of our approach. This work contributes practical insights into state and parameter selection, estimation method modifications for differential algebraic equation (DAE) systems, and data pre-processing in real-world settings.
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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 | 0.001 |
| 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