Biomass Combustion With Emphasis on Interactions Between Metals and Inorganic Particulate
Bibliographic record
Abstract
Biomass is clean, stored solar energy. Not only is it a plentiful fuel, but its use also reestablishes the natural carbon cycle helping mitigate greenhouse gas emissions. This renewable energy source is nearly CO2 neutral. Overall, it is possible to achieve a 93% reduction in net CO2 emissions per unit heating value by switching from coal to biomass and a 84% reduction by switching from natural gas-fired cogeneration to biomass. Due to inherent advantages of the biomass in substituting fossil fuels, and increasing legislative pressures against CO2 emissions (Kyoto Protocol), biomass-based power is genuinely considered. It seems practically impossible to meet Kyoto requirements by replacing fossil fuels combustion with nuclear energy, hydropower or fuel cells. Simply, there is not enough time. In this context, there exists a niche for the biomass-based power generation. This paper compares interactions between metals and solid particles evolving from biomass during the classical Fluidized Bed Combustion (FBC) and a new Low-High-Low temperature (LHL) combustion. Experiments, conducted at a pilot-scale, reveal a clear pattern of surface predominance of light metals (Ca, K) and core predominance of heavy metals (Cd, Cr) within the LHL-generated particles. No such behavior was induced by the classical FBC approach. Metals migration is linked to the evolution of inorganic particles. A composite picture of the metals rearrangements in the particles was obtained by the combination of independent analytical techniques including electron probe microanalysis, field emission scanning electron microscopy, inductively-coupled plasma spectrometry and X-ray diffractometry. It is suggested that the combination of (i) the high-temperature region in the LHL and (ii) changes in the surface free energy of the particles is the driving force for the metal-particle behavior. Important practical implications of the observed phenomena are proposed including removal of hazardous submicron particulate and reduction in fouling/slagging during biomass combustion. These findings may contribute to redesigning currently operating FBC units in order to generate non-hazardous, non-leachable, re-usable particles where heavy metals are immobilized while environmental and technological problems reduced.
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 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".