Scaling process intensification technologies: what does it take to deploy?
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.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".