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Record W3080818333 · doi:10.1002/cjce.23871

Process intensification connects scales and disciplines towards sustainability

2020· article· en· W3080818333 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsPolytechnique Montréal
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekCanada Research Chairs
KeywordsGlobeProcess (computing)SustainabilityScale (ratio)Computer scienceProcess managementData scienceManagement scienceEngineeringGeographyPsychologyEcology

Abstract

fetched live from OpenAlex

Abstract Process intensification (PI) has been established as a cluster of technologies able to produce more with less. While scientists around the globe advocate for new semantics that are increasingly tied to the notion of sustainability, what does the literature data say about PI? A Vosviewer bibliometric map of PI displays it as closely linked to the subjects of design, optimization, gas‐to‐liquid technologies, mass transfer, catalysis, and kinetics. We analyze the relationship between PI and these subjects while identifying misconceptions about the intensifying potential of some of them, as is the case for process optimization. We provide examples and summarize the recent technological trends for all these cases. Finally, we provide an outlook on the future of PI in which we identify elements that will be key to accelerate the adoption of PI technologies at the commercial 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.209
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it