Real-Time Modelling of Dynamic Behaviour for Clinker Rotary Kilns: <i>Learning from Experiment and Theory</i>
Bibliographic record
Abstract
The authors’ objective is to estimate a dynamic behaviour of Clinker Rotary Kiln when the state variables of the process can be measured only at a few locations. These variables (gas, clinker temperatures and clinker mass distributions) are elaborated with the help of heat, pressure and mass balance partial differential equations. The resulting state model, decomposed into five phenomenological zones of CRK, is used as a first step to define a set of Basic Operating Functions. A second step is used to identify a set of Operating Functions. These OFs have also been decomposed into longitudinal distributions of CRK to replace the constant, unknown or unmeasured parameters. Based on the feature of each zone, the OFs are obtained by solving the steady-state model using the standard Newton-Raphson procedure. The CRK is thus characterized by the state variables and intelligent software of the Numerical Estimated Operating Functions and method is proposed to reevaluate the set of the state variables, in adequate space step-size. Consequently, the state variables profiles are linked with the corresponding OFs, which have a direct influence on submitted disturbances for calibration.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".