Effect of ash composition on adsorption and agglomeration characteristics in low‐low‐temperature electrostatic precipitator systems
Bibliographic record
Abstract
Abstract The low‐low temperature electrostatic precipitator (LLT‐ESP) has been applied in many power plants to improve the emission performance. In this study, a number of fix‐bed experiments were conducted, which simulated the operation condition of LLT‐ESP. The effect of chemical composition on the adsorption characteristics of ash samples and the agglomeration after adsorption were studied. The results show that the agglomeration of ash particles occurs in three forms, including agglomeration among small particles, adhesion of loose pellets to large particles, and agglomeration among large particles. The capacity of ash particles to adsorb sulphuric acid is less affected by the increase of added amounts of NaCl or KCl. At the same time, the added amount of NaCl or KCl also has little effect on the agglomeration degree of ash particles. The addition of MgO, CaO, Fe 2 O 3 , Na 2 CO 3 , or K 2 CO 3 from 0% to 13.3%, 22.4%, 23.9%, 18.4%, or 14.2%, promotes the adsorption of sulphuric acid by ash particles from 1.95 mg g −1 to 7.33, 7.71, 6.72, 5.1, or 5.44 mg g −1 (based on sulphur content), respectively, thereby promoting the agglomeration between ash particles. As the addition amount increases, the agglomeration phenomenon among particles becomes more conspicuous. The form of agglomeration changes from agglomeration among small particles to agglomeration among large particles. In addition, the addition of MgO or CaO has great influence on the adsorption and agglomeration.
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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) | 0.000 | 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) | 0.001 | 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".