Effect of the Reactor Material on the Reforming of Primary Syngas
Bibliographic record
Abstract
Syngas, mostly hydrogen and carbon monoxide, has traditionally been produced from coal and natural gas, with biomass gasification later emerging as a renewable process. It is widely used in fuel synthesis through the Fischer–Tropsch (FT) process, where the H2/CO ratio is crucial in determining product efficiency and quality. In this sense, this study aimed to reform an emulated syngas resulting from the supercritical water gasification of biomass, tailoring it to meet the H2/CO ratio required for FT synthesis. Conditions resembling dry reforming were applied, using temperatures from 600 to 950 °C and steel wool as a catalyst. Additionally, the effects of Inconel and stainless steel as reactor materials on syngas reforming were investigated. When Inconel was used, H2/CO ratios ranged between 1.04 and 1.84 with steel wool and 1.28 and 1.67 without. When comparing reactions without steel wool performed either in the Inconel or the stainless steel reactors, those using Inconel consistently outperformed the stainless steel ones, achieving CH4 and CO2 conversions up to 95% and 76%, respectively, versus 0% and 39% with stainless steel. It was concluded that the Inconel reactor exhibited catalytic properties due to its high nickel content and specific oxides.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".