Demonstration Of Selective Catalytic Reduction For The Control Of NO<sub>x </sub>Emissions From High-Sulfur Coal-Fired Boilers, Project Performance Summary. Clean Coal Demonstration Program
Bibliographic record
Abstract
This project is part of the U.S. Department of Energy's (DOE) Clean Coal Technology Demonstration Program (CCTDP) established to address energy and environmental concerns related to coal use. DOE sought cost-shared partnerships with industry through five nationally competed solicitations to accelerate commercialization of the most promising advanced coal-based power generation and pollution control technologies. The CCTDP, valued at over five billion dollars, has significantly leveraged federal funding by forging effective partnerships founded on sound principles. For every federal dollar invested, CCTDP participants have invested two dollars. These participants include utilities, technology developers, state governments, and research organizations. This project is one of 16 selected from 55 proposals submitted in response to the CCTDP's second solicitation. Southern Company Services, Inc. (Southern), a subsidiary of Southern Company, conducted the Demonstration of Selective Catalytic Reduction for the Control of NOx Emissions from High-Sulfur, Coal-Fired Boilers project. Gulf Power Company's Plant Crist provided the host site and the Electric Power Research Institute (EPRI) and Ontario Hydro co-funded the project and provided technical input. The project began in June 1990 and operational testing occurred between July 1993 and July 1995. In simplistic terms, the selective catalytic reduction (SCR) process consists of injecting ammonia (NH3) into boiler flue gas and passing the flue gas through a catalyst bed where the oxides of nitrogen (NOx) and NH3 react to form water vapor and nitrogen (an element that comprises 78 percent of air).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".