Partitioning of Mercury and Other Trace Elements From Coal and Waste-Derived Fuels During Fluidised Bed Pyrolysis
Bibliographic record
Abstract
The potential releases of toxic trace elements such as mercury, lead and arsenic call for emission control during fluidised bed (FB) combustion, pyrolysis or gasification of waste-derived fuels and fossil fuels. Control measures for sulphur oxides, nitrogen oxides and particulates effectively remove many other pollutants from the exhaust gases as well, but mercury and several other trace elements are already problematic and this situation will only worsen with time. Besides the effect of temperature, gas atmosphere and halogens, the presence of other species, for example metal oxides, have an effect on under which conditions and in what form trace elements are released from fuels. Understanding the events of trace elements release from solid fuels during the pyrolysis or char combustion stage will provide a key to manipulating their partitioning and controlling their emissions. Pyrolysis experiments were made with coal, sewage sludge and automotive shredder residue (ASR) in a two-stage fluidised bed combustion (FBC) facility. An Ontario Hydro measurement train plus an additional sampling system were used to measure mercury and around fifteen other trace elements in the gases, and also char samples were taken and analysed. Results from these experiments are presented. An issue that is addressed explicitely is the bed material, which may be contaminated with significant amounts of toxic trace elements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.004 | 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 teacher head, 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".