Développement de nouveaux catalyseurs partiellement biosourcés pour la synthèse Fischer-Tropsch à partir d'un biogaz
Bibliographic record
Abstract
The Gold Horizon 2020 project aims to integrate phytoremediation of soil contaminated by heavy metals and the production of clean fuels. In its Canadian section, contaminated biomass is pyrolyzed in an autothermal fluidized bed reactor (BFB-ATP), enabling metals to be concentrated in the charcoal. The pyrolysis gas, characterized by a low H₂/(CO+CO₂) ratio, is then steam reformed to produce synthesis gas, subsequently used for Fischer-Tropsch synthesis (FTS) into fuels. This research focuses on the development and evaluation of new catalyst formulations for steam reforming and FTS. Hydroxyapatite (HAp) was selected as the catalyst support due to its stability and promising textural properties, with nickel as the active metal for steam reforming and iron for FTS. Eggshell-derived hydroxyapatite (HApE), synthesized by co-precipitation, was compared with commercial hydroxyapatite (HApC). The steam reforming study revealed that Ni/HApC catalyst exhibits good thermal stability and enhanced resistance to carbon deposition. The 10% Ni/HApC catalyst achieved nearly complete methane conversion (99%) and a stable hydrogen yield of 90%. The optimization of operating conditions through statistical analysis highlighted the influence of temperature, space velocity and steam/methane ratio on hydrogen yield. Pyrolysis gas reforming was simulated on Aspen Hysys, then tested experimentally. At 800°C, with a steam to hydrocarbon ratio of 9, a H₂/(CO+CO₂) ≈ 1 ratio was achieved, meeting the minimum required for Fischer-Tropsch synthesis using iron-based catalysts. The SFT study in a three-phase CSTR reactor showed that the 10Fe/HApC exhibited stable CO conversion (35%) with high selectivity for C₅₊ hydrocarbons (>90%), favoring the production of gasoline and olefins. Compared with a commercial catalyst (Nanocat®), 10Fe/HApC demonstrated better resistance to sintering, due to the strong interaction between iron and hydroxyapatite, leading to iron phosphide formation. Finally, 10Fe/HApC showed high activity and stability for CO₂ hydrogenation with an H₂/CO₂ feed ratio of 3 and a temperature of 280°C, with marked selectivity for liquid hydrocarbons. During hydrogenation of the syngas from steam reforming with a H₂/(CO₂+CO) = 1 ratio, the 10Fe/HApE catalyst showed better performance than 10Fe/HApC, with a 25% CO₂ conversion, attributed to its higher basicity related to a higher Ca/P ratio, favoring dissociative CO₂ adsorption and oxygenate formation. These results highlight the potential of hydroxyapatite as a sustainable catalyst support for optimizing the performance of steam reforming and Fischer-Tropsch synthesis processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.006 |
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; both teacher heads agree on what is shown here.
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".