Review of catalytic syngas production through steam or dry reforming and partial oxidation of studied liquid compounds
Bibliographic record
Abstract
Although natural gas is the main feedstock for the production of hydrogen and syngas, liquid hydrocarbons and oxygenated compounds are of interest for reasons associated with their local availability and their easiness to be stored and transported. This review focuses on steam, dry, and partial oxidation of liquid feedstock. The vast research work published in these fields does not allow for a full coverage of the entire literature. Instead, the authors present from both scientific and technical stand points the knowledge which seems more promising toward eventual improvements of commercial units and utilization of new catalytic formulations. Since traditional steam reforming is relatively very well covered by other reviews, this review has mainly focused on the relatively recent works on glycerol, a biodiesel production by‐product, and the widely available and distributed commercial diesel/biodiesel. New promising catalytic formulations are proposed and are actually under testing for eventual commercial use. Nevertheless these catalysts might be eventually efficient for gaseous (e.g., CH4) hydrocarbons conversion to syngas. Dry and partial oxidation has also been reviewed both globally and in an incremental way. All liquid feedstock tested are reported. Finally, this review tries to bridge the gap between fundamental and factual research in this field. Both are important but the interpretation of the results remains a strong function of each paper's main focus. This review does not pretend that this gap is fully bridged but it has the ambition to help the researchers as well as the practitioners in this area to synthesize the existing knowledge. WIREs Energy Environ 2016, 5:169–187. doi: 10.1002/wene.167 This article is categorized under: Bioenergy > Science and Materials Fuel Cells and Hydrogen > Science and Materials
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".