Treatment of Sydney Tar Pond Sludge in CFBC
Bibliographic record
Abstract
Test burns of mixtures of Sydney tar pond sludge and coal were carried out using CETC’s mini-circulating fluidized bed combustor (mini-CFBC). The goal was to determine if CFBC technology could be used to treat the tar pond sludge. During the tests, CO2, O2, CO, SO2, and NOx in the flue gas were monitored continuously. Stack gas sampling was carried out for HCl, metals, particulate matter, VOCs, total hydrocarbons, semi-volatile organic compounds, dioxins and furans and PCBs. Results showed that HCl, Hg, particulate matter, PCDD/Fs and metal concentrations were all below both the current limits and the gas release limits to be implemented in 2008 in Canada. Sulphur capture efficiency was about 89–90%. The percentage of fuel nitrogen converted to NOx was of the order of 4.7 to 6.1, which is significantly lower than that of conventional pulverized coal-fired boilers and well within normal range of FBC boilers. PCB and PAH emissions levels were comparable or lower than levels reported in the literature for industrial-scale FBCs. VOC concentrations were low except for benzene, for which the concentration was higher than that reported for utility-scale FBC and pulverized coal-fired boilers. In addition, CO concentration was high at 1200 to 2200 ppm. However, these CO concentrations are typical of CETC’s mini-CFBC firing coal. The trials showed that, for 10% by weight tar pond sludge mixed with 90% by weight coal, the combustion was both stable and efficient. The tests demonstrated that CFBC technology could be an environmentally sound option for eliminating wastes from the Sydney tar pond.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".