Combustion Characteristics of Biomass Ash and Lignite Blend Under Oxy-Fuel Conditions
Bibliographic record
Abstract
In this study an attempt was done to profoundly explore the pyrolysis and combustion behaviors and emission characteristics of lignite samples in O2/N2 and O2/CO2 (oxy-fuel conditions) ambients. A special focus was allocated to the effects of three inorganic materials, potassium (K), calcium (Ca) and iron (Fe) on combustion characteristics of Turkish lignite using non-isothermal Thermo-gravimetric Analysis (TGA) technique combined with Fourier Transform Infrared (FTIR) spectroscopy and the effects of ambient gases and various oxygen mole fractions were considered. Eventually the co-processing combustion tests of lignite and the ash contents of different biomass fuels were investigated and the possible way of using biomass as a potential source of inexpensive catalysts in combustion processes were discussed. Co-processing combustion tests of lignite and biomass ash contents indicated that the hazelnut shell and walnut shell ash contents were significantly effective in increasing the char reactivity of lignite due to high concentration of potassium based oxides during combustion tests carried out in both air and 30% O2 in CO2 ambients. Furthermore the catalytic reactivity of wheat straw and cattle manure ash contents were observed in the second region of combustion regarding volatile matter release and combustion in both air and 30% O2 in CO2 ambients. These results are thought to be due to high concentrations of Alkali and Alkaline earth metals existed in the impregnated lignite samples with wheat straw and cattle manure ash contents and especially Na-based oxides in the cattle manure form. Finally in the case of lignite sample impregnated with saw dust ash content, it was observed that the impregnated lignite was significantly more reactive in devolatalization process in 30% O2 in CO2 ambients. These results revealed that the ash contents of walnut and hazelnut shell biomass fuels can be used as a potential source of inexpensive K-based catalysts in the co-processing of coal and biomass ash. Furthermore high concentrations of Alkali and Alkaline earth metals existed in the ash contents of biomass fuels like wheat straw, cattle manure and saw dust can make them suitable sources of inexpensive catalysts and develop a step forward in economic aspects of catalytic coal combustion.
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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".