Scaling process intensification technologies: what does it take to deploy?
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
• Key enablers for scaling up process-intensification (PI) technologies are identified • PI technologies rely less on the electricity grid vs. traditional ones • Interdisciplinary collaborations and lab-to-market partnerships are crucial • Business development experts should be involved at early TRL (3, 4) • Techno-economic analysis and LCA de-risk business development at TRL 3, 4 Process intensification (PI) has emerged as a transformative approach to enhancing efficiency, sustainability, and economics across chemical and manufacturing industries. However, within its dedicated communities, there is recognition of a persistent gap in transitioning these innovations from laboratory-scale success to widespread industrial adoption. Scaling up PI technologies is far more complex than simply replicating laboratory conditions on a larger scale. Challenges such as the integration with existing units and processes, proving economic viability, and navigating regulatory requirements often impede the practical implementation of PI innovations. This paper aims to identify the key enablers for scaling up PI technologies by presenting a roadmap to bridge the gap between concept and commercialization. While robust engineering design frameworks and advanced modeling tools are crucial, interdisciplinary collaborations and lab-to-market partnerships (or integrated scaling collaborations) are equally critical to drive the successful adoption of PI at the industrial scale.
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 | 0.002 |
| 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