Production de biobutanol à partir de lignocellulose : un nouveau procédé thermochimique
Bibliographic record
Abstract
In the last years, the energy demand has increased and a large pourcentage of this energy is obtained from fossil fuels, but the use of these fuels has generated CO2 emissions and environmental pollution. For this reason, this research was focused on the use of alternative energies from lignocellulosic biomass to produce renewal fuels decreasing CO2 gas emissions. Canada is a country with high quantities of lignocellulosic biomass which can represent a cheap source for the high value added molecules and fuels production. The first part of the study focuses on the kinetic study of the production of methyl levulinate and levulinic acid from cellulose with a homogeneous catalyst (H2SO4). The second part study the conversion of cellulose to levulinates (platform molecule) using a homogeneous catalyst and a heterogeneous catalyst (Al2(SO4)3). The third part is devoted to study the hydrolysis of methyl levulinate to levulinic acid using copper-based catalysts. Analytical techniques such as SEM, XRD, TPX were used to study the supported catalysts and understand their effect on the reaction. The fourth part of the project relates to the study of the production of 2-butanol from lignocellulosic biomass through the production of methyl levulinate and levulinic acid which are platform molecules and potentially substitutes for biodiesel. Thereafter, the levulinic acid is decarboxylated to 2-butanone and the latter is reduced to 2-butanol using bifunctional catalysts (such as Ru/C and Pt/C) under mild conditions. All of this work contributes to understanding the reactions of the new butanol production process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".