Bibliographic record
Abstract
The reduction of mill effluent discharge as a result of stringent environmental legislations can have a significant impact on sodium (Na) and sulfur (S) balances in the kraft pulping process. In order to maintain a proper balance of Na and S, kraft mills may need to adopt different makeup strategies. For this purpose, a dynamic model was developed to predict the Na and S balance in the kraft recovery cycle, as well as the accumulation of undesirable non-process elements such as chlorine (Cl) and potassium (K). The model was developed using the CADSIM software and was validated using data obtained from a Brazilian bleached kraft pulp mill. The calculated data from the model showed good agreement with mill data with respect to all parts of the mill simulated. Dynamic tests designed to calculate the white liquor sulfidity over specific periods of time also presented good agreement. The result indicates that the model is able to describe the balance of chlorine, potassium, sodium and sulfur in the kraft process. A study conducted to evaluate the Cl and K accumulation agrees with the expected behaviour observed in mill data. The presence of ash treatment systems allow to reduce Cl and K contents in recovery boiler precipitator ash from 4.2 mol% Cl(Na+K) to 1.25 mol % and from 2.25 mol % K/(Na+K) to 0.8 mol% for 100% ash treated. The tests performed for Na and S balances focused in the makeup requirement for two situations: ash purging and ash treatment to control Cl and K levels. The use of ash treatment systems reduced Na and S makeup requirement from 5 to 50% depending on the amount of ash treated. A simple mathematical model was then used to estimate the Cl balances around the recovery cycle. Given that the proper simplifications are applied, the CADSIM model and the CSTR model presented good agreement in estimating the Cl balances. This result provided not only another method for the CADSIM model to be validated but also a way to calculate a rough estimate for Cl balance.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".