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Machine Learning Predicts Efficiency of Micropollutant Removal
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Scientists at the NIEHS-funded North Carolina State University Superfund Research Program Center created machine learning models that can help predict how well granular activated carbon can clean up contaminated water. With his student Yoko Koyama, Detlef Knappe, Ph.D., developed models that consider properties of the micropollutants — such as PFAS and volatile organic compounds — specific characteristics of the water being treated, and features of different GAC types.
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