Decarbonizing the aluminium industry: A comprehensive review of pathways and process integration perspectives
Bibliographic record
Abstract
Aluminium is recognized as an essential material for the global energy transition. However, its production is extremely energy-intensive and largely dependent on fossil fuels. The aluminium industry emits more than 1.11 Gt CO 2 -eq annually. Multiple obstacles persist in the face of decarbonizing such a heavy industry, including a shortage of alternative technologies for high-temperature furnace applications, unavailability of renewable electricity sources sufficient to supply continuous power loads, and inefficiencies of material recycling pathways. This review focuses on critically evaluating these challenges and defining methods to overcome them. A decarbonization framework is proposed for the aluminium industry through four interconnected layers: process integration, energy and exergy efficiency, techno-economics, and life cycle assessment (LCA). Twenty decarbonization metrics are computed across the four layers of this framework. The most relevant of which are process energy load, renewability index, carbon balance, total cost, and technology readiness level. Key mitigation strategies such as carbon capture, biomass use, and grid decarbonization, are found to collectively drive emission reductions in the aluminium sector. This review addresses key knowledge gaps in the literature and offers a structured framework to support strategic decision-making across the aluminium value chain.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".