Bibliographic record
Abstract
Abstract Concepts such as process intensification, distributed manufacturing, and modularity are becoming mainstream as the chemical industry has to meet the demand for growth while concurrently facing sustainable development challenges. To meet economies of scales, modularity appeals to the concept of numbering up (scaling down and then scaling out). As numbering up becomes more common and a necessity, investors look at solid financial predictors to reduce the uncertainty around the fate of their assets. Traditional economic models that either scale up or scale down the investment for a plant size with a power law (exponent α) of a reference unit at a given capacity (Q) and its investment (I) are valid for the several identical plants and their components. When it comes to scaling down and then numbering up, the investment, or rather price of a modular plant the exponent relating price and capacity is β = 1/n − 1. We report a case study to scale down a 1000 barrel/day (bbl/day) micro‐refinery gas‐to‐liquid unit to convert wasted natural gas to Fischer‐Tropsch fuels. The investment for 100 units 100 times smaller approaches the cost of the same production capacity given by a single 1000 bbl/day unit costing $1 million.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".