MétaCan
Menu
Back to cohort
Record W1969885604 · doi:10.1155/2015/572983

Model-Based Control of a Continuous Coating Line for Proton Exchange Membrane Fuel Cell Electrode Assembly

2015· article· en· W1969885604 on OpenAlexaff
Vikram Devaraj, Luis Felipe López, Joseph J. Beaman, Serge Prudhomme

Bibliographic record

VenueInternational Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsPolytechnique Montréal
FundersOffice of Naval ResearchMultidisciplinary University Research Initiative
KeywordsCoatingProton exchange membrane fuel cellMaterials scienceMembrane electrode assemblyController (irrigation)ElectrodeMembraneProcess engineeringComposite materialChemical engineeringFuel cellsEngineeringElectrolyteChemistry

Abstract

fetched live from OpenAlex

The most expensive component of a fuel cell is the membrane electrode assembly (MEA), which consists of an ionomer membrane coated with catalyst material. Best-performing MEAs are currently fabricated by depositing and drying liquid catalyst ink on the membrane; however, this process is limited to individual preparation by hand due to the membrane’s rapid water absorption that leads to shape deformation and coating defects. A continuous coating line can reduce the cost and time needed to fabricate the MEA, incentivizing the commercialization and widespread adoption of fuel cells. A pilot-scale membrane coating line was designed for such a task and is described in this paper. Accurate process control is necessary to prevent manufacturing defects from occurring in the coating line. A linear-quadratic-Gaussian (LQG) controller was developed based on a physics-based model of the coating process to optimally control the temperature and humidity of the drying zones. The process controller was implemented in the pilot-scale coating line proving effective in preventing defects.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Chemical EngineeringSame topicFuel Cells and Related MaterialsFrench-language works237,207