Optimization of Alternative Options for SO2 Emissions Control in the Ontario Power Generation
Bibliographic record
Abstract
The power generation sector is considered as one of the major contributors to air pollution. There are different air emissions that can be emitted from power generation and among these are SO2 emissions which will be the focus of this research. In this study, an optimization model was formulated and written in a general format. The objective of this model is to select the best pollution control strategy for the power generation to reduce SO2 to specific level while meeting the electricity demand at minimum cost. Three different mitigation options were considered to reduce SO2 and these are: fuel balancing, switching and implementing different control technologies. The model is illustrated in a case study taken from Ontario Power Generation; Canada. The results show that when we consider two options (fuel balancing and switching), the optimum option for SO2 reduction is fuel switching for higher reduction targets (up to 75%).On the other hand, for the case in which all options are considered, the results show that applying FGD technology is the best option to reduce SO2 emissions and it can achieve up to 85% SO2 reduction. Sensitivity analysis was carried out in this case study, and the result indicates that the only affected variable is the total annualized cost.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".