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Record W7057991370

LIFE CYCLE ASSESSMENT OF HYDROGEN PRODUCTION FROM CANADIAN BIOMASS USING FORMIC ACID AS AN ENERGY CARRIER FOR TRANS-ATLANTIC ENERGY EXPORT

2024· dissertation· en· W7057991370 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typedissertation
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFormic acidLife-cycle assessmentEnergy carrierHydrogen economyBiomass (ecology)Hydrogen productionFossil fuelHydrogenHydrogen fuel
DOInot available

Abstract

fetched live from OpenAlex

The importance of Hydrogen (H2) in current global energy systems is undeniable. Moving from the energy systems depending on fossil fuel to energy systems that are carbon-free is a necessity, thus solutions such as hydrogen economy is required. Especially after recent geopolitical challenges in Europe which could make the energy acquisition a crucial problem. Liquid organic hydrogen carriers (LOHC) are applicable pathways for transitioning H2 into energy and to avoid the storage and transportation limitations of gaseous and liquid H2. Formic acid (FA) is an attractive alternative for such purposes due to its minimal level of toxicity and its significant volumetric storage capacity for H2. In this study, a Life cycle assessment (LCA) of the supply chain involving the Trans-Atlantic export of energy from Canada to Germany is conducted using formic acid as a LOHC and OxFA process to convert biomass to formic acid. The environmental impacts of all units and processes involved in this supply chain are examined, and the results are compared against other traditional systems for hydrogen production. A sensitivity analysis was also performed to recognize the crucial contributors and assess the processes and units that impose considerable influence on the overall environmental impact.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.727

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.009
GPT teacher head0.213
Teacher spread0.203 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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