Mercury Emissions during Cofiring of Sub-bituminous Coal and Biomass (Chicken Waste, Wood, Coffee Residue, and Tobacco Stalk) in a Laboratory-Scale Fluidized Bed Combustor
Bibliographic record
Abstract
Four types of biomass (chicken waste, wood pellets, coffee residue, and tobacco stalks) were cofired at 30 wt % with a U.S. sub-bituminous coal (Powder River Basin Coal) in a laboratory-scale fluidized bed combustor. A cyclone, followed by a quartz filter, was used for fly ash removal during tests. The temperatures of the cyclone and filter were controlled at 250 and 150 degrees C, respectively. Mercury speciation and emissions during cofiring were investigated using a semicontinuous mercury monitor, which was certified using ASTM standard Ontario Hydra Method. Test results indicated mercury emissions were strongly correlative to the gaseous chlorine concentrations, but not necessarily correlative to the chlorine contents in cofiring fuels. Mercury emissions could be reduced by 35% during firing of sub-bituminous coal using only a quartz filter. Cofiring high-chlorine fuel, such as chicken waste (Cl = 22340 wppm), could largely reduce mercury emissions by over 80%. When low-chlorine biomass, such as wood pellets (Cl = 132 wppm) and coffee residue (Cl = 134 wppm), is cofired, mercury emissions could only be reduced by about 50%. Cofiring tobacco stalks with higher chlorine content (Cl = 4237 wppm) did not significantly reduce mercury emissions. This was also true when limestone was added while cofiring coal and chicken waste because the gaseous chlorine was reduced in the freeboard of the fluidized bed combustor, where the temperature was generally below 650 degrees C without addition of the secondary air. Gaseous speciated mercury in flue gas after a quartz filter indicated the occurrence of about 50% of total gaseous mercury to be the elemental mercury for cofiring chicken waste, but occurrence of above 90% of the elemental mercury for all other cases. Both the higher content of alkali metal oxides or alkali earth metal oxides in tested biomass and the occurrence of temperatures lower than 650 degrees C in the upper part of the fluidized bed combustor seemed to be responsible for the reduction of gaseous chlorine and, consequently, limited mercury emissions reduction during cofiring. This study identified the important impacts of temperature profile and oxides of alkali metal (alkali earth metal) on mercury emissions during cofiring in the fluidized bed combustor.
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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) | 0.001 | 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 | 0.001 | 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".