Modelling of a clinker rotary kiln using operating functions concept
Bibliographic record
Abstract
Abstract Modelling and parameter identification of complex dynamic systems/processes is one of the main challenging problems in control engineering. An example of such a process is clinker rotary kiln (CRK) in cement industry. In the prevailing models independently of which structure is used to describe the kiln's dynamics and the identification algorithm, parameters are assumed to be centralised and constant while the CRK is well known as a distributed parameter system with a strongly varying dynamic through time. In this work, the kiln's dynamic is described in the form of a state‐space representation with three state variables using a system of partial differential equations (PDE). The structure is chosen so that it can easily be embedded in classical state‐space control algorithms. The parameters of the PDE system are called operating functions since their numerical values vary with respect to different operating conditions of the kiln, to their position in the kiln, and through time. A phenomenological approach is also proposed in this paper to identify the operating functions for a given steady‐state operation of the kiln. The model is then used to perform a semi‐dynamic simulation of the process through manipulating main process variables.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".