Performance of Promoted Iron/CNT Catalyst for Fischer–Tropsch Synthesis: Influence of Pellet Shapes and Binder Loading
Bibliographic record
Abstract
Instead of powder, catalyst pellets are preferred in a fixed-bed reactor for Fischer–Tropsch synthesis (FTS). In this research, promoted iron catalysts supported on carbon nanotubes (CNT) were pelletized in two different shapes (spherical and cylindrical) using different bentonite loadings (10–20 wt %). The pellets were tested for mechanical strength using single pellet and bulk crushing test methods (ASTM D 4179, 6175, and 7084). The spherical pellets were found to sustain higher crushing strength as compared to cylindrical pellets. However, both spherical and cylindrical Fe/CNT pellets with 20 wt % bentonite loading were found to be mechanically stronger than commercial alumina spherical pellets, suggesting their suitability in fixed bed reactors. The pellets were characterized to determine the change in active metal dispersion and ease in reducibility based on their shape and binder content using CO chemisorption, XRD, and H 2 -TPR. The cylindrical shaped pelleted catalysts were found to be easy to reduce as compared to spherical shaped pelleted catalysts. All pellets were tested for FTS in a fixed bed reactor at 270 °C and 300 psi with syngas (H 2 /CO = 2.0) flowing at rate of 60 mL/min. The 20 wt % bentonite loaded Fe/CNT pellet was found to be stable with highest CO conversion and C 5+ selectivity among all pelleted catalysts. The decline in CO conversion in pelleted Fe/CNT catalyst containing 10–15 wt % bentonite loading could be due to the structural instability of these materials during reaction. The internal mass transfer calculations estimated severe diffusional limitations in spherical pellet catalysts resulting in sharp decline in their FTS activity. Hence, cylindrical pelleted catalyst containing 20 wt % bentonite was found to be best performing among the series and its performance for FTS was optimized for temperature, pressure, and GSHV using the Taguchi method.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".