Effects of process parameters and selective heating on microwave pyrolysis of lignocellulosic biomass for biochar production.
Bibliographic record
Abstract
Biochar has successfully emerged as a solid biofuel to address the concerns of greenhouse gas emissions. This research investigated microwave pyrolysis of maple wood in a laboratory-scale microwave pyrolysis reactor to study the effects of final pyrolysis temperature, holding time and selective heating on the biochar yield through microwave absorbers. A regression model was developed to predict the biochar yield as a function of pyrolysis temperature, holding time and doping ratio. The analysis indicated that microwave heating can fasten the process of pyrolysis conversion reactions and the yield of the pyrolysis products increased with increase in holding time and decrease in process temperature. On the other hand, variation in doping ratio did not have a significant effect on the biomass conversion to biochar. The biochar was analyzed through proximate analysis and differential scanning calorimetry (DSC) to determine its thermodynamic potential. A biochar sample can be characterized as a carbon-rich solid fuel with high fixed carbon content or residual matter but low volatile or volatile matter. The proximate analysis indicated that the highest residual matter (%) to volatile matter (%) ratio was obtained for the pyrolysis temperature of 290°C, holding time of 1 min and dope ratio of 24% while biochar produced at pyrolysis temperature of 250°C, holding time of 1 min and dope ratio of 32% had the highest energy in the DSC analyses. The regression model developed indicated that the predicted values for the exothermic energies were in good agreement to the observed values (P ≤ 0.05).
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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 | 0.001 |
| 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 | 0.001 |
| 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".