Enhancing Carbon Reactivity in Mercury Control in Lignite-Fired Systems
Bibliographic record
Abstract
This project was awarded through the U.S. Department of Energy (DOE) National Energy Technology Laboratory Program Solicitation DE-PS26-03NT41718-01. The Energy & Environmental Research Center (EERC) led a consortium-based effort to resolve mercury (Hg) control issues facing the lignite industry. The EERC team-the Electric Power Research Institute (EPRI); the URS Corporation; the Babcock & Wilcox Company; ADA-ES; Apogee; Basin Electric Power Cooperative; Otter Tail Power Company; Great River Energy; Texas Utilities; Montana-Dakota Utilities Co.; Minnkota Power Cooperative, Inc.; BNI Coal Ltd.; Dakota Westmoreland Corporation; the North American Coal Corporation; SaskPower; and the North Dakota Industrial Commission-demonstrated technologies that substantially enhanced the effectiveness of carbon sorbents to remove Hg from western fuel combustion gases and achieve a high level ({ge} 55% Hg removal) of cost-effective control. The results of this effort are applicable to virtually all utilities burning lignite and subbituminous coals in the United States and Canada. The enhancement processes were previously proven in pilot-scale and limited full-scale tests. Additional optimization testing continues on these enhancements. These four units included three lignite-fired units: Leland Olds Station Unit 1 (LOS1) and Stanton Station Unit 10 (SS10) near Stanton and Antelope Valley Station Unit 1 (AVS1) near Beulah and a subbituminous Powder River Basin (PRB)-fired unit: Stanton Station Unit 1 (SS1). This project was one of three conducted by the consortium under the DOE mercury program to systematically test Hg control technologies available for utilities burning lignite. The overall objective of the three projects was to field-test and verify options that may be applied cost-effectively by the lignite industry to reduce Hg emissions. The EERC, URS, and other team members tested sorbent injection technologies for plants equipped with electrostatic precipitators (ESPs) and spray dryer absorbers combined with fabric filters (SDAs-FFs). The work focused on technology commercialization by involving industry and emphasizing the communication of results to vendors and utilities throughout the project.
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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 | 0.000 | 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) | 0.001 | 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 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".