Towards carbon-negative primary aluminium production: Integrating biomass resources and renewable electricity
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".