Bibliographic record
Abstract
Water is vital to the mining industry; mines can require substantial amounts of water and are often located in some of the driest places on earth. Reducing water withdrawals and improving mine water use are key strategic requirements for moving toward a more sustainable mining industry. Mine water requirements often have significant technical, economic, environmental and political implications. This thesis quantifies global mine water withdrawals and discusses methods of improving mine water use by reducing water withdrawals and water-related energy consumption. The thesis is composed of four main sections. First, two methods are proposed to calculate global mining water withdrawals by commodity. One method is based on the amount of water required to process a tonne of ore and the other is based on the amount of water required to produce a tonne of concentrate. A large database was created, compiling data regarding ore production, commodity production, commodity prices, and mine water withdrawals between 2006 and 2009. The study estimates that global water withdrawals range from 6 to 8 billion m3 per annum. Second, the thesis presents a case study on the challenges faced and lessons learned during the design, start-up and modification of the water systems of a large copper mine site. Third, the thesis identifies multiple mine water reduction, reuse and recycle strategies that have been implemented around the world. A model is developed and used to show the potential impact of these strategies. The results of the modelling show how a hypothetical mine could reduce water withdrawals from 0.76 m³/t to 0.20 m³/t of ore processed or lower. In particular, the combination of ore pre-concentration and filtered tailings disposal reduced water consumption by over 74% of the base case. Finally, this thesis describes and demonstrates a method of determining the lowest energy option for a mine water network. The method uses a linear programming algorithm to compare options for matching water sources with consumers at mine sites. An example illustrates the method and shows how mine water system energy requirements can be reduced by over 50%.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| 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".