A Case Study of the Selective Catalytic Reduction (SCR) System at the Algonquin Power Energy-From-Waste Facility
Bibliographic record
Abstract
The Algonquin Power Energy-From-Waste (APEFW) facility is located in the suburban Toronto, Ontario city of Brampton. It receives approximately 140,000 metric tonnes (154,000 tons) of MSW per year from the Region of Peel (Region) and approximately 10,000 metric tonnes (11,000 tons) per year of international airport waste from the area’s two international airports. The APEFW facility commenced initial operations in 1992 and included four, 91 tonne (100 ton) per day Consumat two stage incinerators with heat recovery boilers and a dual-train air pollution control (APC) system consisting of evaporative cooling towers, venturi reactors and fabric filter baghouses. The APEFW facility expanded its capacity in 2001 with the addition of a fifth 91 tonne (100 ton) per day modular incinerator and heat recovery boiler. One of the stipulations in the permitting process was that the entire expanded facility meet more stringent emission standards that included a significantly lower nitrogen oxides (NOx) emission rate. After a review of several available NOx control technologies, the APEFW facility chose to install a Selective Catalytic Reduction (SCR) system. While SCR systems are fairly common on EFW facilities in Europe, the APEFW facility is the only EFW facility in North America that currently operates with an SCR system and as such has gained valuable insight into the application and performance of this technology that is very relevant to the North American EFW industry. This paper discusses the operation and maintenance of the SCR system, compares pre- and post-SCR NOx emissions and presents capital and operating costs for the SCR including the cost per tonne of waste processed and the cost per tonne of NOx removed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".