Gasification performance of torrefied Timothy hay and spruce wood chars in a CO<sub>2</sub> environment
Bibliographic record
Abstract
Abstract Timothy hay abundantly available in New Brunswick, Canada, is mostly used for animal feed and bedding. Upgrading biomass using Torrefaction method can offer benefits in its waste management, energy density and energy conversion efficiency. Temperature and residence time play an important role in the torrefaction process. Meanwhile, CO2 gasification is also a promising thermochemical conversion process due to its potential to reduce net GHG emissions and tune syngas composition. This study investigates the impact of torrefaction parameters on isothermal and non‐isothermal CO2 gasification of Timothy hay and spruce chars. Timothy hay chars exhibited higher CO2 gasification reactivity than chars from spruce. The physicochemical properties analysis indicated that higher reactivity of Timothy hay char was mainly attributed to the high amount of alkali and alkaline earth metal (AAEM) content, relatively large BET surface area, a high number ofactive sites, and a low crystalline index. Moreover, in both experimental cases, char derived through a high heating rate and high residence time conditions exhibited improved gasification performance, which was attributed to the generation of large amounts of AAEM (Ca and K) and high specific surface area. Co‐gasification results during non‐isothermal processes under CO2 showed the presence of larger interactions in coal char/Timothy hay char blends than that of coal char/spruce char blends. For both experimental conditions, interactions were enhanced once the char prepared from high heating rate and high residence time was gasified with coal char. Thus, the proposed approach is a sustainable way of conversion of Timothy hay under CO2 environment.
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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".