From Agro-Industrial Waste to Gold Lixiviant: Evaluating Cassava Wastewater Applications in Artisanal Mining
Bibliographic record
Abstract
Artisanal and Small-Scale Gold Mining (ASGM) is a primary source of global mercury pollution, creating an urgent need for sustainable, low-cost alternatives to amalgamation. This study investigates the use of cassava wastewater (manipueira), a cyanogenic agricultural byproduct, as a lixiviant for a gold concentrate (14.30–15.87 ppm Au) from an artisanal mine. Two approaches were evaluated: direct leaching with manipueira in natura (250 ppm CN−) in single and double 8 h and 12 h cycles, and leaching with a cyanide solution concentrated from dilute manipueira (100 ppm CN−) via a simplified air-stripping system. Results were benchmarked against the mine’s amalgamation (44.7% recovery) and 30-day heap leach (75.8% recovery) processes. The most effective method observed was a two-cycle, 8 h leach with manipueira in natura, which achieved a mean gold recovery of 76.75±4.71%. This result is comparable to the efficiency of the site’s lengthy heap leach process and suggests a promising, faster, route to eliminating mercury use. Longer (12 h) leaching cycles yielded lower recoveries, suggesting process limitations such as preg-robbing. The cyanide concentration method proved inefficient, recovering a maximum of 12.40% of the available cyanide and resulting in a weaker lixiviant. The findings demonstrate that while direct leaching is a viable alternative to mercury, the inherent instability of manipueira necessitates a focus on developing efficient, controlled systems to extract and concentrate its cyanide content, thereby creating a standardized “green” reagent from a large-volume agricultural waste stream.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".