Chemical Composition Determination at the Bottom Region of a Recovery Boiler Furnace by Direct Minimization of Gibbs Free Energy
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Bibliographic record
Abstract
An optimization strategy has been applied to describe the chemical composition at the furnace bottom in the Kraft recovery boiler of a pulp production process. The concentrations of each involved chemical species were calculated through an optimization approach, minimizing the Gibbs free energy of the system. Various systems were proposed and tested, assuming different chemical species and phases number. Because serious initialization problems were found at this stage for some of the proposed systems, an optimization heuristic method (PSO) was used for the first approach to the problem. Once the appropriate phases number and chemical species in the system were determined, the initialization problems disappeared and the use of a deterministic optimization method (SQP) became viable. The proposed approach has shown to be satisfactory to reproduce industrial data and also data reported in the open scientific literature. On a employé une stratégie d'optimisation pour décrire la composition chimique dans la partie basse du four de la chaudière de récupération de pâte kraft d'un procédé de production de pâte. Les concentrations de chaque espèce chimique concernée ont été calculées par optimisation, ce qui permet de minimiser l'énergie libre de Gibbs du système. Divers systèmes sont proposés et testés, en supposant des espèces chimiques et un nombre de phases différents. Étant donné que d'importants problèmes d'initialisation ont été constatés à ce stade pour certains des systèmes proposés, une méthode d'optimisation heuristique (PSO) a été utilisée comme première approche au problème. Une fois que le nombre de phases et les espèces chimiques appropriés ont été déterminés, les problèmes d'initialisation ont disparu et le recours à une méthode d'optimisation déterministe (PSQ) s'est avéré viable. La méthode proposée s'avère satisfaisante pour reproduire les données industrielles ainsi que les données présentées dans la littérature scientifique ouverte.
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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 | 0.000 | 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