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Aprendizaje automático aumentado por datos mejora el diseño del tratamiento de agua: Predicción precisa de la

Jiaqi Wu¹, Yanzhou Ding¹, Chengfei Zhu¹

¹State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, 200237 Shanghai, China.

|December 31, 2025

Resumen

Un nuevo marco de aumento de datos que utiliza autoencoders variacionales (VAE) mejora los modelos de aprendizaje automático para predecir reacciones de radicales contaminantes en el tratamiento de aguas residuales. Este método mejora la precisión y proporciona información molecular clave para la optimización de procesos.

Sus antecedentes:

Los procesos avanzados de oxidación/reducción (AOPs/ARPs) mediados por radicales son efectivos para la degradación de contaminantes en aguas residuales.
La predicción cinética precisa y la comprensión mecanicista son cruciales para la optimización de AOPs/ARPs.
Los modelos de aprendizaje automático (ML) a menudo luchan con conjuntos de datos pequeños, lo que lleva a un rendimiento e interpretación deficientes.

Conclusiones:

El marco de aumento de datos desarrollado basado en VAE aborda eficazmente las limitaciones de muestras pequeñas en ML para aplicaciones ambientales.
El estudio proporciona información valiosa a nivel molecular para optimizar los procesos de tratamiento de aguas residuales mediados por radicales.
Este enfoque ofrece una estrategia generalizable para mejorar el rendimiento y la interpretabilidad de los modelos de ML en cinética química y ciencias ambientales.

Palabras clave:

Radical de dióxido de carbono Aumento de datos Aprendizaje automático Productos farmacéuticos y de cuidado personal Autoencoder variacional

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