Comparative Life Cycle Assessment of Conventional and Dry Stack Tailings Disposal Schemes: A Case Study in Northern China
Bibliographic record
Abstract
Alternative tailings disposal technologies can be effective solutions to mining waste safety and environmental problems. The current decision-making processes for tailings disposal schemes lack consideration of environmental impacts. Based on a case study of an open-pit iron mine in northern China, this study adopted the life cycle assessment (LCA) method to compare the environmental impacts of three tailings disposal schemes of conventional slurry disposal technology (CSDT), dry stack disposal technology (DSDT) by belt conveyance and DSDT by truck transport. The results indicated that (1) the environmental impacts of the CSDT scheme were lowest under the premise that water consumption was ignored; (2) the environmental impacts of the DSDT scheme by belt conveyance mainly originated from its transport process, indicating that the tailings storage facilities (TSFs) site planning could be crucial in design decision making; (3) the environmental impacts of the DSDT scheme by truck transport mainly originated from the energy consumption of dry stacking equipment; and (4) the DSDT scheme by truck transport was eventually found to be preferable and implemented in the case study, after comprehensively considering the LCA results, TSF safety and remaining capacity, and social and policy uncertainties. It is therefore recommended to conduct LCA of environmental impacts in the decision-making process for the sustainable design of TSFs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".