Distributed production: Scale‐up vs experience
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 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.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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