Bibliographic record
Abstract
The global inventory of tailings is currently >55 billion m3 and is forecast to increase to almost 70 billion m3 by 2025. The cost of these tailings includes the construction and maintenance of tailings storage facilities (TSFs), the liability of catastrophic releases, environmental damages, and erosion of social licence to continue mining. Billions of dollars and decades of research have been spent searching for improved technologies to better dewater tailings and reclaim TSFs. The ElectroKinetic Solutions Inc. (EKS) tailings dewatering technology (EKS-DT) process has been developed to dewater legacy and fresh tailings in situ, using electrokinetics. Over more than a decade of lab and field-scale testing has significantly improved the technology’s dewatering effectiveness and reliability and confirmed its commercial viability. The process is less costly compared to current tailings management technologies and excels in its ESG performance. EKS has shown through a field demonstration that very fine-grained tailings (i.e. average particle size <6 microns) with an initial solids content of 20% (kg/kg) can be dewatered to >60%. EKS has also produced a forecasting model. The model accurately forecasts dewatering time and energy consumption for different designs and operating schedules, critical information for designing commercial installations. A 1,700 m3 field demonstration from 2019 to 2021 proved that the technology is commercially viable. During this time, over 1,100 m3 of water was removed from the tailings and the energy consumption was <15 kWh per m3 of water released. The process was operated year-round through two harsh winters. The process operated as well during the ice-covered period as during warmer months. The installation was operated continuously by the automated control system without an onsite operator. The design of the first commercial installations of the technology is now underway. The engineering design process for the field demonstration and for commercial systems will be discussed, including the role of the EKS model.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".