FINITE ELEMENT MODELING OF SELECTIVE HEATING IN MICROWAVE PYROLYSIS OF LIGNOCELLULOSIC BIOMASS
Bibliographic record
Abstract
Abstract—Microwave pyrolysis overcomes the disadvantages of conventional pyrolysis methods by efficiently improving the quality of final pyrolysis products. Biochar, one of the end products of this process is considered an efficient vector for sequestering carbon to offset atmospheric carbon dioxide. The dielectric properties of the doping agents (i.e., char and graphite) were assessed over the range of 25◦– 400◦C and used to develop a finite element model (FEM). This model served to couple electromagnetic heating, combustion, and heat and mass transfer phenomena and evaluated the advantages of selective heating of woody biomass during microwave pyrolysis. The dielectric properties of the doping agents were a function of temperature and decreased up to 100◦C and thereafter remained constant. Regression analysis indicated that char would be a better doping substance than graphite. The simulation study found that doping helped to provide a more efficient heat transfer within the biomass compared to non-doped samples. Char doping yielded better heat transfer compared to graphite doping, as it resulted in optimal temperatures for maximization of biochar production. The model was then validated through experimental trials in a custom-built microwave pyrolysis unit which confirmed that char doping would be better suited for maximization of biochar.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".