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Record W4416163631 · doi:10.1016/j.jclepro.2025.146994

Towards carbon-negative primary aluminium production: Integrating biomass resources and renewable electricity

2025· article· en· W4416163631 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cleaner Production · 2025
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsRenewable energyAluminiumBiocharCarbon footprintSmeltingProduction (economics)Renewable resourceBiomass (ecology)

Abstract

fetched live from OpenAlex

Decarbonizing heavy industries such as aluminium production is critical for achieving global climate targets. Despite increasing recycling rates, demand for primary aluminium is projected to increase by 20% over the next 25 years. Primary aluminium is largely produced via the Hall–Héroult process, which is both energy-intensive, consuming 13,000–15,000 kWh E E /t A l , and dependent on fossil-derived carbon anodes. This results in a global average carbon footprint of 12–15 t CO 2 /t Al , with the International Aluminium Institute reporting a value of 14.8 t CO 2 /t Al in 2023, 60% of which was produced in China. This study aims to synthesize and evaluate decarbonization pathways for primary aluminium production by investigating alternative alumina reducing agents (biochar and renewable hydrogen) and assessing their integration with secondary aluminium processes and a district heating network. The analysis is conducted through a total site optimization framework that incorporates waste heat recovery, seasonal resource switching, and biogenic CO 2 mineralization. Results indicate that a net-zero to net-negative carbon footprint can be achieved, ranging from –0.5 to 0.2 t CO 2 /t Al for the presented case study. Biomass-based pathways were found to deliver the highest CO 2 abatement potential, while electricity-dependent pathways face higher costs and grid-related emissions. Among the evaluated options, the bio-hydrogen scenario achieves the most favourable balance between cost, energy use, and environmental performance. These findings demonstrate that carbon-negative aluminium production is feasible through the integration of renewable resources and process-system optimization. • Fossil-free aluminium smelting is enabled using biochar or renewable hydrogen. • Integrated biomass gasification supplies on-site fuels and reducing agents. • Waste heat recovery increases efficiency and allows onsite electricity generation. • Site-wide optimization results in net-negative aluminium production. • Bio-hydrogen route delivers lowest cost and highest renewable energy share.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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