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Record W2756705971 · doi:10.1109/sege.2017.8052821

Exploring the potential of power-to-gas concept to meet Ontario's industrial demand of hydrogen

2017· article· en· W2756705971 on OpenAlexaffabout
Abdullah Al-Subaie, Ali Elkamel, Ushnik Mukherjee, Michael Fowler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPower to gasBase load power plantEnvironmental scienceEnvironmental economicsGreenhouse gasRenewable energyWork (physics)HydrogenElectricity generationProcess engineeringHydrogen storageElectricityIndustrial gasWaste managementNatural resource economicsPower (physics)EconomicsElectrolysisEngineeringChemistryElectrical engineeringDistributed generationMechanical engineering

Abstract

fetched live from OpenAlex

Hydrogen is an essential commodity in the refining and chemical industry. Hydrogen is commonly produced from the mature technology steam methane reforming (SMR), which has a drawback of releasing significant greenhouse gas emissions. The power-to-gas (PtG) concept, on the other hand, can produce green hydrogen through water electrolysis by utilizing Ontario's electricity grid powered mostly by CO2-free sources during off-peak demand. Also, PtG is a novel energy storage concept, which can effectively manage the surplus baseload generation issue encountered in the province. Therefore, this work explores the potential of implementing PtG to meet the demand of industrial hydrogen in Ontario. The paper examines the surplus power resulted from the off-peak net exports to neighboring jurisdictions and curtailed power from wind and nuclear during the years 2014-2016 as well as the surplus forecast for the next 15 years. Then, it quantifies the hydrogen volumes upon employing PtG versus the demand. The analysis shows that PtG energy storage concept has the potential to supply industrial users with the majority of the demand, particularly when making use of Ontario's available seasonal storage of depleted gas wells and salt caverns at least for the next four years.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.751

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 science0.0010.000
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 head0.068
GPT teacher head0.246
Teacher spread0.178 · 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 designBench or experimental
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

Citations8
Published2017
Admission routes2
Has abstractyes

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