Study of the Pyrolysis of Straw Biomass for Bio-oil Production and its Catalytic Upgrading
Bibliographic record
Abstract
This thesis presents a comprehensive study of the pyrolysis of lignocellulosic biomass (sawdust, wheat, oat, flax and barley straws). In the first part of the study, the pyrolysis of sawdust was observed using a thermogravimetric analyzer (TGA), in order to understand the devolatilization process and to obtain its global kinetic parameters. The influences of particle size, initial weight of the sample and heating rate on the devolatilization of sawdust particles were assessed. It was observed that the pyrolysis of sawdust differed significantly with variations in heating rate. As the heating rate increased, the char yield also increased. The kinetic parameters, including activation energy (E), frequency factor (k0) and order of reaction (n), for the two stages considered in the model were: EA2 = 79.53 (kJ/mol), EA3 = 60.71 (kJ/mol); k02 = 1.90 × 106 (1/min), k03 = 1.01 × 103 (1/min); n2 = 0.91, n3 = 1.78, respectively. In the second part of the study, the pyrolysis of several Canadian straw biomasses was studied using a TGA and a bench-scale horizontal fixed-bed reactor. The effects of various catalysts on product yields are discussed. When using zeolite catalysts, the bio-oil and bio-char yields of the straw pyrolysis increased to 46.44% and 38.77%, respectively, while the gas yield was decreased to 13.65%. The use of the catalyst zeolite ZY-SS had the most significant effect on overall bio-oil and bio-char yields, increasing the bio-oil yield by about 2% and the bio-char yield by 8%. A screening of different catalysts unveiled that Ni-Mo/TiO2 was the most active catalyst. The structure was investigated using Brunauer-Emmett-Teller (BET) surface area showed that the Ni-Mo/TiO2 catalyst presented a higher surface area and more optimal mesopores than Ni-V/TiO2. Based on the results, it appears that the significant hydrodeoxygenation (HDO) activity for both the catalysts attributed to a high dispersion of metals and acidic sites, which was affected by the interaction between the nickel and the titania support. The activation energy (E) values for guaiacol reactions over Ni-Mo/TiO2 and Ni-V/TiO2 were 93.6 and 98.4 kj/mol, respectively; and, the E values for anisole reactions over Ni-Mo/TiO2 and Ni-V/TiO2 were 80.9 and 53.9 kj/mol, respectively.
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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".