Tar abatement using dolomites during the gasification of pine sawdust
Bibliographic record
Abstract
Biofuels like ethanol are gaining serious momentum because of concerns over climate change and the rising cost of fossil fuels.Saskatchewan is the first province in Canada to pass a law requiring ethanol blended into its gasoline.A blend rate of 7.5% is mandated as of January 2007.This legislation is not yet fully enforced as ethanol production cannot currently meet demand, but local production is increasing.The traditional method of production is via grain fermentation; however the food versus fuel debate indicates this is unethical when food shortages and prices are already on the rise.Gasification is a robust technology for processing raw, non-food grade biomass into syngas (H 2 and CO) which can then be further converted to ethanol via gas-to-liquid conversion technology.Condensable materials called tars form during gasification and must be further converted to gaseous products to avoid problems downstream.This can be achieved via optimization of process conditions and catalysis.The research for this thesis was carried out in two phases.Phase 1 examined the effects of process conditions on the noncatalytic temperature-programmed gasification of wood (Jack Pine) biomass.Temperature was varied from 700 to 825 o C, water flow rate was varied from 2 to 5 cm 3 /h, and N 2 flow rate from 16 to 32 cm 3 /min.When varying biomass gasification conditions, overall % carbon conversion to gaseous products reached a maximum of 70% at 825 o C, 5.0 cm 3 /h H 2 O, and 32 cm 3 /min N 2 .670 cm 3 product gas per g biomass was produced, with 35.8 mol% H 2 and H 2 :CO of 1.56.In Phase 2, catalytic gasification of wood biomass was carried out using a double bed micro reactor in a twostage process.Temperature programmed steam gasification of biomass was performed in the first bed at 200-850 o C. Following in the second bed was isothermal catalytic decomposition gasification of volatile compounds (including tars).Dolomites from Canada, Australia and Japan were examined for their effects on tar abatement and the overall gaseous product.The gasification of pine sawdust resulted in 74% of carbon emitted as volatile matter during tar gasification (200-500 o C biomass bed temperature).4.16 (a) overall H 2 production and (b) H 2 :CO ratio for dolomites 4.17 Overall H 2 :CO ratio as a function of Fe wt% for Canadian dolomites 4.18 Performance of Canada #1 (a) during tar abatement and (b) overall, catalyst bed temperatures of 650 -800 o C. 4.19 Effect of space velocity on Canada dolomite #1 at 750 o C 4.20 Effect of re-using Canada #1 dolomite in subsequent runs at 800 o C.*N 2 adsorption gives porosimetry from 1.2 to 30 nm pore diameter ( micropores < 2nm), and is also used for surface area measurements ** Hg Porosimetry measures pores from 6 to 32400 nm (macropores >50 nm) ***Specific Gravity = volume per unit mass BET S.A. = N 2 BET Surface Area; Avg.Pore D. = Average Pore Diameter; Vol.= Volume
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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".