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Record W3048356725 · doi:10.1016/j.procir.2020.02.139

Integration of life cycle assessment with energy simulation software for polymer exchange membrane (PEM) electrolysis

2020· article· en· W3048356725 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.

Bibliographic record

VenueProcedia CIRP · 2020
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsScope (computer science)Polymer electrolyte membrane electrolysisLife-cycle assessmentSoftwareElectrolysisSystems engineeringComputer scienceEngineeringProcess engineeringRisk analysis (engineering)Production (economics)BusinessChemistry

Abstract

fetched live from OpenAlex

In the assessment and planning of energy systems, use of simulations is a crucial step. They allow the quantification of techno-economic potential to subsequently aid decision-making amongst various technological or design choices. In these software, environmental analysis is either too simplified or neglected completely since a conventional life cycle assessment (LCA) study might not be feasible to account for different variabilities related to scale, scope, energy carriers, etc. In this paper, we integrate techno-economic analysis of hydrogen production from polymer exchange membrane (PEM) electrolysis with life cycle assessment. This step-by-step guideline will be useful especially for professionals working in energy system design to develop a LCA model for PEM in their respective software. Consequently, this will enable them to take environmental indicators into account while planning facilities.

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)0.000
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: none
Teacher disagreement score0.884
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.247
Teacher spread0.229 · 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