ArticleBiodegradation2026
Efficient removal of Ni, Al, and As from water using Micrococcus yunnanensis: batch experiments and explainable machine learning.
Article in Biodegradation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
In this study, a bacterial strain of Micrococcus yunnanensis was isolated from the discharge water of an organized industrial zone wastewater treatment plant and used in the bioremoval of Al, Ni, and As. The effects of different experimental conditions such as initial metal concentration, pH, temperature, contact time, and biomass concentration on metal bioremoval were evaluated, and peak removal conditions were identified. The highest removal rates were 80, 68, and 27.33% for Al, Ni, and As, respectively. The isotherm and kinetic results for all metals showed that they fit the non-linear Langmuir and pseudo-second-order models, respectively. Ten representative machine-learning algorithms from linear, tree-based, ensemble, kernel, instance-based, and neural-network approaches were comparatively evaluated using a combined multi-metal dataset. The Extra Trees model was found to exhibit the best generalization performance (CV R
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