A decision support tool formulti‐pollutants reduction incement industry using analytic hierarchy process (AHP)
Bibliographic record
Abstract
Abstract Several energy intensive industries are contributing to air pollution problems. Cement industry, as an example, is one of these significant sources of several air pollutants and these must be monitored and controlled. This paper deals with five air pollutants from cement plants and these are SO2, NOx, CO2, dust, and volatile organic compounds (VOC). The purpose of this study is to evaluate several available technologies to control each pollutant. Analytic hierarchy process (AHP) technique is used as a decision support tool to find the best technology for each pollutant under multi‐criteria. These criteria are: cost, efficiency, lifetime or duration, and industry acceptability. The technique is illustrated in a case study from St. Marys Plant, located in St. Marys, Ontario, Canada. The results show that adsorption on activated coke technology will be recommended for SO2 reduction. For NOx reduction, the AHP suggests to apply selective non‐catalytic reduction (SNCR) technology based on the four criteria defined. Carbon capture and sequestration (CCS) using MEA technology is chosen for CO2 reduction. For dust reduction, bag filters should be used and increase oxygen concentration at the kiln inlet is the selected technology for VOC reduction. The current paper covers the set of criteria weights considered typical for cement plants. The results presented here are illustrative and user defined weighting is required to make this study valuable for a specific group of users.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".