MétaCan
Menu
Back to cohort
Record W4403277671 · doi:10.1016/j.ifacol.2024.09.278

Comparative study of centralized and decentralized systems for hydrogen hubs

2024· article· en· W4403277671 on OpenAlexaff
Henrique G. Nunes, Mohammad Asghari, Hamid Afshari, Mohamad Y. Jaber

Bibliographic record

VenueIFAC-PapersOnLine · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of TorontoDalhousie University
Fundersnot available
KeywordsDistributed computingComputer scienceDecentralised systemBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigates hydrogen as a clean and versatile energy carrier. Doing so addresses the need for sustainable energy and bridges a research gap in the literature: the efficiency of centralized versus decentralized hub systems. The challenge of hydrogen supply chain configuration is explored, seeking to minimize its financial and ecological effects. The contribution of this work lies in a comparative analysis using a bi-objective optimization model underpinned by simulations for transportation and storage logistics. Results indicate that the centralized system had better outcomes for cost and emissions when compared to the decentralized system in the simulations conducted.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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 teacher head, 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIFAC-PapersOnLineSame topicElectric Vehicles and InfrastructureFrench-language works237,207