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 SO 2 , NO x , CO 2 , 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 SO 2 reduction. For NO x 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 CO 2 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 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 | 0.003 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".