Rotary cement kiln coating estimator: Integrated modelling of kiln with shell temperature measurement
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
Abstract Coating thickness protection in the burning zone of a rotary cement kiln during operation is important from the viewpoint of the kiln productivity. In this paper, an integrated model is presented to estimate the coating thickness in the burning zone of a rotary cement kiln by using measured process variables and scanned shell temperature. The model can simulate the variations of the system, thus the impact of different process variables and environmental conditions on the coating thickness can be analysed. The presented steady‐state model derived from heat and mass balance equations uses a plug flame model for simulation of gas and/or fuel oil burning. Moreover, the heat transfer value from shell to the outside is improved by a quasi‐dynamic method. Therefore, at first, the model predicts the inside temperature profile along the kiln, then by considering two resistant nodes between temperatures of the inside and outside, the latter measured by shell scanner, it estimates the formed coating thickness in the burning zone. The estimation of the model was studied for three measured data sets taken from a modern commercial cement kiln. The results confirm that the average absolute error for estimating the coating thickness for the cases 1, 2, and 3 are 3.26, 2.82, and 2.21 cm, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it