Micro‐syngas technology options for GtL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it