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Machine learning (ML) reveals the key factors influencing the removal of chlorinated hydrocarbons (CAHs) by nanoscale zero-valent iron (nZVI)
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DOI：10.1016/j.eti.2025.104728.png)
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Abstract
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• Machine learning identifies reaction time and specific surface area as key factors • The reaction occurs within 0.25 days and stabilizes after about 4 days at 120 m²/g. • The economical dosage is ~0.2 g/L with a concentration sensitivity ~25 mg/L. • Bicarbonate ＜500 mg/L inhibits the process, whereas copper ions enhance reactivity.
Keywords:
Chlorinated hydrocarbons
Nanoscale zero-valent iron
Remediation mechanisms
Machine learning
Environmental factor quantification
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