Environmental assessment of materials in preparing alkali-activated materials using bauxite residue
Bibliographic record
Abstract
Abstract Aluminum is a highly produced metal globally. In the built environments alone, aluminum is known for its light weight and anti-corrosive properties and is a common choice of material in window frames, curtain walls, and light fixtures. The metal is processed from its oxide, alumina, the production of which releases a caustic byproduct known as bauxite residue (BR). BR is stored in massive reservoirs, however, due to the sheer volume of production, industries handling the material are reaching their storage limits. It is of high importance to research methods to valorize BR. Researchers have proposed a multitude of valorization methods, which include metal recovery from the byproduct, and using BR as raw material in production of another material. However, it is counterproductive to expend considerable amounts of energy and use excessive raw materials to manage utilization of BR. This study compared a method of valorization of bauxite residue with clay bricks. The method of preparing alkali-activated materials (AAM) with bauxite residue as a precursor was semi-quantitatively analyzed to compare the raw materials in the AAM with clay bricks. The materials were analyzed for climate change, ozone depletion, human toxicity, and aquatic ecotoxicity using OpenLCA software and the ecoinvent version 3.4 database. While the alkali activators possessed high values of the environmental impact when measured by the kilogram, the impacts were greatly reduced due to their small percentage in weight contained in the AAM. This study is the first stage of analyzing the environmental costs of valorizing bauxite residue. The AAM showed low environmental impact in material use when impacts of toxicity are considered. However, it displayed a higher climate change impact than that of clay bricks. Transport of raw materials to the preparation site played a significant role in the analysis. The next step is to compare the energy used in preparing AAMs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".