MétaCan
Menu
Back to cohort
Record W2529568545 · doi:10.1109/sege.2016.7589531

Decarbonizing transportation through the use of power-to-gas for oil refining operations

2016· article· en· W2529568545 on OpenAlexaff
Abdullah Al-Subaie, Azadeh Maroufmashat, Sean Walker, Michael Fowler, Ali Elkamel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRefining (metallurgy)RefineryOil refineryHydrogen productionProcess engineeringSteam reformingEnvironmental scienceWaste managementPower to gasElectrolysisHydrogenEngineeringMaterials scienceChemistryElectrolyte

Abstract

fetched live from OpenAlex

Power-to-Gas is a technology that generates hydrogen by electrolysis. It can be used to provide a number of energy services including energy storage, ancillary services for the electrical grid and, the production of hydrogen for industrial processes and transportation fuel. Hydrogen has many industrial applications such as in oil refining where it is used primarily in hydrotreating and hydrocracking processes. The purpose of this paper is to provide an incentive for using power-to-gas technology for oil refining processes in an effort to reduce the carbon footprint in refining industry and ultimately the transportation sector. It also highlights the optimal size and operation of the hydrogen production facility that include polymer electrolyte membrane (PEM) electrolyzers to meet the proposed refinery demand. The economic calculations include comparison of the cost of hydrogen ($ per kg) between electrolysis and steam methane reforming. The carbon pricing is also incorporated in the analysis to show its potential impact in the costing of both technologies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.994

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 head0.063
GPT teacher head0.267
Teacher spread0.204 · 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 designNot applicable
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

Citations9
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicHybrid Renewable Energy SystemsFrench-language works237,207