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Record W4291465019 · doi:10.3390/hydrogen3030019

Hybrid Renewable Hydrogen Energy Solution for Remote Cold-Climate Open-Pit Mines

2022· article· en· W4291465019 on OpenAlexaff
Hosein Kalantari, Seyed Ali Ghoreishi‐Madiseh

Bibliographic record

VenueHydrogen · 2022
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHaulageRenewable energyHydrogen vehicleEnvironmental scienceGrid energy storageBattery (electricity)ElectricityWind powerGreenhouse gasPhotovoltaic systemDiesel fuelWaste managementAutomotive engineeringOpen-pit miningEnergy storageEngineeringDistributed generationHydrogen fuelElectrical engineeringMechanical engineeringMining engineeringFuel cells

Abstract

fetched live from OpenAlex

Contemporary off-grid mining operations rely on diesel fuel for the provision of their total energy including electricity, heat, and haulage. Given the high cost of diesel and its imposed greenhouse gas emissions, mining companies are looking for more affordable and cleaner sources of energy for their operations. Although renewable energy systems, such as solar photovoltaic and wind provide efficient solutions to address this challenge, full decarbonization has shown to be very challenging, mainly due to the high cost of battery storage along with the inability to meet total site energy demand. Integrating hydrogen and thermal storage with battery banks can facilitate a full transitioning off diesel. In this sense, the present study intends to offer an innovative decarbonized solution by integrating wind turbines with a multi-storage system (battery, hydrogen, and thermal storage) to supply the total energy (electricity, heat, and haulage) for remote open-pit mines. Among the different proposed fully decarbonized configurations in this study, it is shown that a renewable system with a hydrogen-powered fleet and hybridized battery/hydrogen storage configuration can present the most economically viable case for open-pit mines with a considerably less life-of-mine cost.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.244
Teacher spread0.223 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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