Bibliographic record
Abstract
Abstract Investigations into direct methane to methanol conversion are justified based on the avoidance of synthesis gas generation, which accounts for around 60% of the capital cost of synthesis gas to methanol conversion. A significant body of information already exists on the process chemistry, but little has been reported on the engineering of such a process. An engineering evaluation of the process was performed and the potential of this process as a platform technology for small‐scale gas‐to‐liquids (GTL) applications was evaluated. It was found that direct methane to methanol conversion had 35% carbon efficiency and 28% thermal efficiency, which were about half of the process efficiencies of indirect methanol synthesis using synthesis gas. The poor process efficiency was mainly a consequence of the irreversible loss of carbon to COx during conversion. The direct methane to methanol process also required an air separation unit, which eroded the stated benefit of avoiding a synthesis gas generation step in the process. The utility footprint was typical of GTL processes, with large gas compression duties and cooling duties. Overall, the engineering evaluation indicated there was no benefit to employ direct methane to methanol conversion instead of indirect methanol synthesis (the industry standard), and there was no specific benefit of direct methane to methanol conversion, irrespective of scale, for GTL applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".