Oxidative torrefaction for cleaner utilization of biomass for soil amendment
Bibliographic record
Abstract
Growing concerns of emissions from wildfires and burning of crop residues demand cleaner and efficient technologies to convert and utilize this residual biomass. The present study demonstrates a pilot scale moving bed biomass torrefaction reactor operating in oxidative medium to produce biochar for soil amendment. A series of experiments are conducted on pine shavings and rice husk, at conditions corresponding to different values of index of torrefaction (Itorr), ratio of higher heating value of torrefied biomass (i.e. biochar) to that of raw biomass. Air-biomass equivalence ratio dominantly governs the operating temperature and affects torrefaction more than the residence time. Product yields of scaled-up reactor differed from those of a smaller bench-top reactor, primarily because of differences in heat transfer within reactor and losses to the surrounding. A relatively linear relationship of Itorr is observed with biochar properties such as specific surface area, water retention capacity, bulk density, and electrical conductivity. When tested for soil amendment, the raw biomass and biochar treatments reduced soil pH by 0.2–0.3 in a season, with lowest pH values in case of pine shavings. Estimated nitrogen release and organic matter decreased with increasing Itorr, but most amendments had no significant effect on seed germination and the number of green shoots. Comparatively, heavy torrefied biomass treatments showed highest shoot heights and crop yield followed by light torrefied or raw biomass and control. Successful demonstration of a pilot scale reactor and encouraging effects on soil and plant growth suggest that commercial-scale oxidative torrefaction of various residual biomass is possible for soil amendment application.
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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".