Fouling Tendency of Ash Resulting From Burning Mixtures of Biofuels
Bibliographic record
Abstract
Specified mixtures of peat with bark and peat with straw were burned in a lab-scale entrained flow reactor that simulates conditions in the superheater region of a biomass-fired boiler. Deposits were collected on an air-cooled probe that was inserted into the reactor at the outlet. For both mixtures, the deposition behaviour followed a non-linear pattern, which suggests that physico-chemical interaction between the ashes of the different fuels has taken place. The results indicate that it is possible to burn up to 30 wt-% bark (renewable biofuel and pulp mill waste) and up to 70 wt-% straw (renewable biofuel and agricultural waste) in mixtures with peat without encountering increased deposition rates in the reactor. The deposit composition was compared to the fuel ash composition using chemical fractionation analysis and SEM/EDX. While the composition of deposits obtained from pure fuels resembles the feed composition, a considerable change is observed in deposits obtained from mixtures. K and S compounds are attached to Si spheres and the substrate surface. The deposition rate is significantly lowered when removing K, S, Cl and Na in bark prior to burning by washing and mechanical/thermal dewatering.
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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) | 0.000 | 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".