ArticleACS omega2022
Application of Machine Learning in a Mineral Leaching Process-Taking Pyrolusite Leaching as an Example.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Gene expression programming for modeling and predicting of leaching of chalcopyrite concentrates using 1-hexyl-3-methyl-imidazolium hydrogen sulfate ionic liquid aqueous solution.Scientific reports · 2026Article
- Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions.RSC advances · 2026Article
- Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.Scientific reports · 2024Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, several machine learning models were used to analyze the process variables of electric-field-enhanced pyrolusite leaching and predict the leaching rate of manganese, and the applicability of those models in the leaching process of hydrometallurgy was compared. It showed that there was no correlation between the six leaching conditions; in addition to the leaching time, the concentrations of sulfuric acid and ferrous sulfate had great influences on the leaching of pyrolusite. The results of the prediction models showed that the support vector regression model has the best prediction performance, with regression index (
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Registered trials
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