Micro‐syngas technology options for GtL
Bibliographic record
Abstract
Abstract Natural gas emissions contribute to climate change, and equally importantly, affect the health of populations near gas fields.[1]At night, the flares from the Bakken fields in North Dakota burn as bright as the lights in cities as large as Minneapolis. Rather than flaring (or worse, venting), this associated natural gas represents a multi‐billion dollar opportunity.[2]Pipelines and liquefying natural gas are cost prohibitive in many cases. Converting methane to fuels is an attractive alternative. We examined three options to convert natural gas to syngas ( and CO), which is the first step to producing fuels: Steam Methane Reforming (SMR), Auto‐Thermal Reforming (ATR), and Catalytic Partial Oxidation (CPOX). Based on a multi‐objective optimization analysis, C hydrocarbon yields are highest with CPOX as the first step followed by Fischer‐Tropsch synthesis (FT). A micro‐refinery with the CPOX‐FT process treating (100 ) natural gas, produces 1300 (8.2 ) of C hydrocarbons. Maximum yields for the SMR‐FT and ATR‐FT processes are 938 and 1100 (5.9 , 7.0 ) of C , respectively. Large‐scale POX and ATR processes produce 1600 L per 2800 kL (10 bbl per 100 MCF) of natural gas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".