Gold Recycling Using Electrochemically-Mediated Liquid-Liquid Extractions
Bibliographic record
Abstract
Electronic waste is the fastest growing waste stream in the world. Although there is roughly $91B worth of precious metals in e-waste, only 20% of precious metals are recovered. Of this amount, $15B is gold. Current processes for precious metal recovery from computer waste rely on activated carbon adsorption, which is not continuous. Recent advances in electrochemical separations have shown promising results using gold-specific solvents to separate gold from random access memory (RAM) chips. Through electrochemical liquid-liquid extractions, we propose a novel technology to process 10,000 annual tonnes of electronic waste, primarily sourcing this waste from the Houston and Austin greater metropolitan areas due to existing Texas legislation that mandate computer recycling. From this design, we are able to recover 8,960 kg of gold per year at a selling price of $70,474. Cyanide is used as the primary solvent to leach gold from the computer chips, and an organic loop composed of 1,1-didodecylferrocene in dibromomethane is used to separate the gold from other metal-cyanide complexes. Furthermore, the implementation of the Sulfidization, Acidification, Recycling, and Thickening (SART) process aids in the recovery of cyanide and minimizes the environmental impact of the process by reducing the amount of cyanide required for leaching. SART also generates copper sulfide as a co-product, recovering 1,750 tonnes per year. Moreover, the implementation of an electrochemical cell in a three-column liquid-liquid extraction system allows the process to run continuously, which is the largest advantage over competing processes. The second largest advantage is that the process runs entirely at standard temperature and pressure avoiding common temperature swing separations that can generate high concentrations of cyanide gas. Assuming a cost of capital of 15%, a plant lifetime of 20 years, and a total capital investment of $90MM, the plant generates a return on investment of 230%, a net present value of $1.15B, and a internal rate of return of 174%. However, it might pose challenges to source 10,000 tonnes of computer chips in the location we have selected. The Royal Mint in the United Kingdom recently partnered with a Canadian startup to recycle computer chips at 4,000 tonnes per year. Scaling this feed to Texas, our plant could alternatively process 585 tonnes of e-waste per year. Under these conditions, the profitability analysis shows an internal rate of return of 57% and NPV of $48MM. Based on this analysis, we recommend adopting this electrochemical liquid-liquid extraction urban mining approach to tap the gold mine currently sitting in landfills across the world.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".