CSTR‐based modelling for the continuous carbonation of sodium aluminate solution
Bibliographic record
Abstract
Abstract A continuous stirred‐tank reactor (CSTR) model has been developed for the continuous carbonation of the sodium aluminates solution (SAS) which is a key process in sintered alumina production. Based on the mechanistic analysis of the continuous carbonation, a kinetic model established at laboratory scale was scaled up to the industrial continuous carbonation of SAS. A CSTR model was formulated for the process under normal operation conditions. The remaining unknown model parameters were estimated from the industrial data and the validation results show that the model can predict the composition concentrations in the carbonated solution by 90% majority with the relative errors within ±5% compared to industrial data. The model developed will be further evaluated for on‐line prediction and control in the corresponding industrial process. Un modèle de réacteur à cuve agitée continu (RCAC) a été conçu pour la carbonation continue de la solution d'aluminate de sodium (SAS), qui est un processus essentiel à la production d'alumine frittée. Selon une analyse mécaniste de la carbonation continue, un modèle cinétique établi à l'échelle laboratoire a été adapté au processus industriel de carbonation continue de la SAS. Un modèle de RCAC a été proposé pour fonctionner dans les conditions normales d'exploitation de ce processus. Les autres paramètres inconnus du modèle ont été estimés à partir des données industrielles et les résultats de la validation ont montré que le modèle peut prévoir les concentrations de la composition de la solution de carbone dans 90 % des cas avec une marge d'erreur de ±5 % par rapport aux données industrielles. Afin d'évaluer plus en détail le modèle proposé, la prédiction et le contrôle en ligne du processus industriel correspondant seront analysés. Can. J. Chem. Eng. © 2010 Canadian Society for Chemical Engineering
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".