Development of Hybrid Fischer-Tropsch Synthesis Catalysts for Direct Production of Synthetic Gasoline from Coal-Based Syngas: An Indonesian Perspective
Bibliographic record
Abstract
The Fischer-Tropsch Synthesis (FTS) represents an environmentally friendly method for producing liquid fuelfrom coal-based syngas via the hydrogenation of carbon monoxide. In order for such a process to be feasible,better catalysts that are capable of enhancing the reaction performance are required. In response to these needs,new catalysts were investigated and introduced in this work. The incorporation of zeolite into the iron based FTScatalyst was expected to help refine the hydrocarbon products and shift the product distribution from the typicalFTS product range to the middle iso-paraffins, which is a gasoline range, and eventually increase the yield of theliquid fuel. This study aims to develop catalyst for producing liquid fuel, particularly gasoline, from carbonmonoxide and hydrogen. The pH of the catalysts was found to have significant effect on the catalytic activity dueto its ability to control the amount of promoter to be precipitated in the catalyst, which results in a lowerreduction temperature. Physically mixing the iron based FTS catalyst with zeolite was found to have little effecton the catalytic activity and the product distribution, apart from slightly increasing the selectivity of iso-paraffins,which is the indication of isomerization activity. Coating of zeolite onto the iron based FTS catalyst to form acore-shell structure was intended to enhance the ease of migration of the reactant and thus increasing thecatalytic activity and shifting the product distribution towards the gasoline range. While zeolite shell has beensuccessfully coated uniformly on the iron based core using hydrothermal synthesis technique, the formation ofthick zeolite shell might have blocked the active FTS sites on the iron based catalyst to some extent and isbelieved to have contributed to the low activity of the core-shell catalyst.
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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".