Advanced control of non‐isothermal axial dispersion tubular reactors with recycle‐induced state delay
Bibliographic record
Abstract
Abstract We develop a delay‐aware estimation and control framework for a non‐isothermal axial dispersion tubular reactor modelled as a coupled parabolic‐hyperbolic PDE system with recycle‐induced state delay. The infinite‐dimensional dynamics are preserved without spatial discretization by representing the delay as a transport PDE and adopting a late‐lumping approach. Closed‐form resolvent operators for the generator and its adjoint are derived, enabling Cayley–Tustin time discretization that maintains key system properties such as stability. The resulting discrete‐time representation is used to design a model predictive controller (MPC) with terminal equality constraints that guarantee closed‐loop stability under input constraints. Concurrently, a moving horizon estimator (MHE) is constructed for output‐based state reconstruction, exploiting inner‐product formulations in the lifted Hilbert space. Estimation and control are integrated via a modular architecture that maintains functional separation while enabling consistent state feedback. Numerical simulations demonstrate that the full‐state MPC successfully stabilizes the unstable reactor under input constraints, while the integrated MHE‐MPC framework enables output‐based feedback control by reconstructing distributed states from noisy measurements, maintaining constraint satisfaction and overall closed‐loop performance.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".