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Record W4323657743 · doi:10.2118/212806-ms

Life Cycle Assessment of Hybrid and Green Hydrogen Generation Models for Western Canada

2023· article· en· W4323657743 on OpenAlexaffabout
Saahil Gupta, Japan Trivedi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHydrogen productionRenewable energyEnvironmental scienceLife-cycle assessmentHydrogen economyGreenhouse gasElectricity generationHydrogen technologiesWind powerHydrogenSteam reformingProcess engineeringWaste managementEnvironmental engineeringEngineeringProduction (economics)ChemistryElectrical engineeringPower (physics)EcologyEconomics

Abstract

fetched live from OpenAlex

Abstract The aim of this paper is to conduct a techno-economic feasibility analysis of adopting a hybrid approach to hydrogen generation. This includes grey hydrogen sourced from natural gas using Steam Methane Reforming (SMR) and green hydrogen from renewable energy. The key focus is on assessing the environmental impacts of such a transition over the next decade in Western Canada while ensuring a clean and stable supply of hydrogen for various industrial processes. A life cycle assessment (LCA) is performed to ascertain greenhouse gas emissions per kg of hydrogen produced. The system boundaries extend from the set up and generation of renewable electricity at standalone and integrated renewable power plants (solar and wind) to the production of hydrogen using water electrolysis. The viability of a site for hydrogen generation from renewables is based on a study of the photovoltaic (PV) and wind potential of various locations in Western Canada. Additionally, an analysis considering the expected improvements in efficiency and scale of upcoming electrolyser technologies is incorporated into the model. Most of the life cycle CO2 emissions of solar and wind sourced hydrogen are from the initial setting up of the power plants. In comparison with SMR sourced hydrogen, total life cycle emissions show a reduction of approximately 90%. As electrolyser technology is improved, hydrogen produced using dedicated renewable sources will achieve price parity over the longer term with the model proposed. It also helps predict the rate at which a hybrid supply of hydrogen can be converted to a primarily green hydrogen supply. These results will serve as a reliable way to transition from grey hydrogen that is currently being produced to green hydrogen, without increasing costs exponentially and with no change in availability. The analysis provides a roadmap for a phased decarbonization of various industries, including the oil and gas industry, where hydrogen is used as a feedstock. Further, it acts as a technical guide to effectuating various hydrogen strategies and achieving emission reduction targets that have been envisaged by provinces in Western Canada.

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: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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
Published2023
Admission routes2
Has abstractyes

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