A Study of the Composition of Carryover Particles in Kraft Recovery Boilers
Bibliographic record
Abstract
Carryover particles are partially/completely burned black liquor particles entrained in the flue gas in kraft recovery boilers. Understanding how carryover particles form and deposit on heat transfer tube surfaces is critically important in the design and operation of a recovery boiler. The tendency for a carryover particle to deposit on a tube surface depends on the particle temperature and composition at the moment of impact. This study was the first to examine systematically how carryover particle composition changes with the black liquor chemistry and burning conditions. \n \nThe effect of black liquor composition and particle size, gas composition (O2 and SO2 concentrations) and temperature on the composition of carryover particles were studied using an Entrained Flow Reactor (EFR). Field studies were conducted on three operating boilers, where an air-cooled probe was used to collect carryover samples at the superheater entrance. \n \nThe results show that the chloride (Cl) and potassium (K) contents in carryover particles were linearly proportional to their contents in black liquor. Cl and K were depleted during black liquor combustion due mainly to the vaporization of NaCl and KCl. The depletion of Cl is about three times greater than that of K. The significant depletion of Cl implies that carryover particles contain much less Cl, and hence, are less sticky than previously expected from black liquor composition. \n \nA dynamic model was also developed to predict the composition of carryover particles as a function of black liquor composition and burning conditions. Based on the data obtained experimentally in this study, the kinetic equations for the oxidation of sulphide available in the literature were modified and incorporated into the model to improve its sulphide and sulphate predictions. The model predicts the main components of carryover particles formed in both the EFR and three operating recovery boilers reasonably well, except for the K content, which is slightly over-predicted at high O2 concentrations (or high particle temperatures). \n \nBased on the predicted composition, it is possible to determine the thermal properties of carryover and to assess its fouling propensity in the boiler. The information helps boiler manufacturers and operators to identify locations in the boiler where massive carryover deposition may occur and to devise appropriate control strategies to minimize fouling and to improve boiler thermal efficiency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".