시멘트 소성시설에서의 수은 배출특성 및 최신 측정방법 적용성 평가 연구
Bibliographic record
Abstract
Recently, there has been growing interest in the emission characteristics and behavior of anthropogenic mercury compounds from emission sources. It is required to establish a standard for reliable mercury measurement method. Therefore, this study has evaluated the applicability of the new measurement method; Continuous Emission Monitoring (US EPA 30A, CEM). In addition, the reliability evaluation was conducted through Ontario Hydro Method (ASTM D6784, OHM) and Sorbent trap method (US EPA Method 30B). As a monitoring result for three months via CEM from cement kiln, the maximum mercury compounds concentration was about 600 μg/S㎥. This is because of the various of raw materials and fuel, and the absence of mercury-control device. The mercury compounds concentrations of OHM, Sorbent trap and CEM were 13.64 (3.33~32.41) μg/Sm3, 13.94 (5.97~23.44) μg/S㎥ and 14.68 (6.19~26.75) μg/S㎥, respectively. The relative standard deviations (% RSD) of the three methods were 5.1~40.9%. The result of this study suggest that it is possible to apply the CEM in the cement kiln when, QA/QC such as calibration is verified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".