Gaseous Contaminant Emissions as Affected by Burning Scrap Tires in Cement Manufacturing
Why this work is in the frame
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Bibliographic record
Abstract
We studied the environmental impact (gaseous emissions) of using scrap tires as a fuel substitute at a cement plant that produces one million tons of cement per year. Using a combination of tires and coal as opposed to only coal caused variations in the pollutant emission rate. The study recorded a 37% increase in the rate of emission for CO, a 24% increase for SO2, an 11% decrease for NOx, and a 48% increase for HCl when tires were included. The rate of emission for metals increased 61% for Fe, 33% for Al, 487% for Zn, 127% for Pb, 339% for Cr, 100% for Mn, and 74% for Cu, and decreased 22% for Hg. On the other hand, the emission rate of organic compounds dropped by 14% for polycyclic aromatic hydrocarbons, 8% in naphthalene, 37% in chlorobenzene, and 45% in dioxins and furans. We used a Gaussian model of atmospheric dispersion to calculate the average pollutant concentration (1-h, 24-h, and annual concentrations) in the ambient air at ground level with the help of the ISC-ST2 software program developed by the USEPA. When tires were used, we observed (i) a 12 to 24% increase in particulate matter, this range considering the concentration variation depending on the average used (1-h, 24-h, and annual basis), 31 to 52% in CO, 22 to 34% in SO2, 39 to 52% in HCl, 12 to 27% in Fe, -3 to 8% in Al, 30 to 37% in Zn, and 270 to 885% in Pb; (ii) a decrease of 8 to 13% in NOx, 9 to 13% in polycyclic aromatic hydrocarbons, 6 to 7% in naphthalene, 32 to 39% in chlorobenzene, and 32 to 45% in dioxins and furans. The results obtained showed that the maximum ground-level concentrations were well within the environmental standards (for operation with only coal as well as for operation with a combination of coal and tires).
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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) | 0.002 | 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