From Agro-Industrial Waste to Gold Lixiviant: Evaluating Cassava Wastewater Applications in Artisanal Mining
Why this work is in the frame
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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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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 | 0.001 |
| 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 it